dequant gemm updare.

testing/python/tilelang/test_tilelang_dequantize_gemm.py

@@ -1,9 +1,35 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+import torch
+import torch.backends
 import bitblas
 from bitblas import tvm as tvm
-from tvm import tl
+from tvm import DataType
+from tvm import tl as TL
+import tvm.tl.language as T
 from bitblas.quantization import _tir_packed_to_unsigned_convert
+from bitblas.tl.utils import get_swizzle_layout
+from bitblas.tl.macro_generator import (
+    TensorCoreIntrinEmitterWithLadderTransform,)
+
+from bitblas.gpu.intrin.lop3 import decode_i4_to_f16
+
+torch.manual_seed(0)
+
+
+def make_swizzle_layout(shared_buf):
+    dtype = shared_buf.dtype
+    shape = shared_buf.shape
+
+    can_swizzle = shape[-1] * DataType(dtype).bits == 512
+    if not can_swizzle:
+        return T.Layout(shape, lambda *args: args)
+
+    def transform_func(i, j):
+        new_warp_i, new_warp_j = get_swizzle_layout(i, j, shape[-1], dtype)
+        return [new_warp_i, new_warp_j]
+
+    return T.Layout(shape, transform_func)
 
 
 def matmul(
@@ -48,7 +74,7 @@ def main(
                 T.copy(A[by * block_M, k * block_K], A_shared)
 
                 for i, j in T.Parallel(block_N, block_K // num_elems_per_byte):
-                    B_shared[i, j] = B[bx * block_N + i, k * block_K // num_elems_per_byte + j] 
+                    B_shared[i, j] = B[bx * block_N + i, k * block_K // num_elems_per_byte + j]
 
                 for i in T.serial(block_N * block_K // num_elems_per_byte // (threads * 4)):
                     for t in T.thread_binding(0, threads, thread="threadIdx.x"):
@@ -101,8 +127,8 @@ def run_gemm(
     )
     print(program)
 
-    mod, params = tl.lower(program)
-    mod = tl.Profiler(mod, params, [2], tl.TensorSupplyType.Integer)
+    mod, params = TL.lower(program)
+    mod = TL.Profiler(mod, params, [2], TL.TensorSupplyType.Integer)
 
     out = mod.run_once()
 
@@ -124,9 +150,296 @@ def ref_program(A, qB):
     mod.assert_allclose(ref_program)
 
 
+def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4(
+    M,
+    N,
+    K,
+    dtypeAB,
+    dtypeC,
+    accum_dtype,
+    transform_b,
+):
+    assert dtypeAB in [
+        "float16",
+        "int8",
+    ], "Currently only float16 and int8 are supported"
+    assert dtypeC in [
+        "float16",
+        "float32",
+        "int32",
+    ], "Currently only float16, float32 and int32 are supported"
+    num_bits = 4
+    num_elems_per_byte = 8 // num_bits
+    storage_dtype = "int8"
+
+    micro_size_x = micro_size_y = micro_size_k = 16
+
+    if dtypeC == "int32":
+        micro_size_k = 32
+
+    # This is a debug config
+    block_row_warps = 1
+    block_col_warps = 4
+
+    warp_rows = 1
+    warp_cols = 2
+    warp_row_tiles = micro_size_x * warp_rows
+    warp_col_tiles = micro_size_y * warp_cols
+    shared_scope = "shared.dyn"
+
+    # Pipeline Stage
+    stage = 2
+    reduce_k = 2
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = 32 if dtypeAB == "float16" else 64
+    chunk = block_K // reduce_k
+
+    is_smooth_a = False
+    can_swizzle = block_K * DataType(dtypeAB).bits == 512
+    apply_pad_a = not (is_smooth_a or can_swizzle)
+    pad_factor = 8
+
+    A_shape = (M, K)
+    B_shape = (N // micro_size_y, K // micro_size_k, micro_size_y,
+               micro_size_k // num_elems_per_byte)
+    A_shared_shape = (block_M, (block_K + pad_factor) if apply_pad_a else block_K)
+    B_shared_shape = (
+        block_N // micro_size_y,
+        block_K // micro_size_k,
+        micro_size_y,
+        micro_size_k // num_elems_per_byte,
+    )
+    C_shared_shape = (
+        block_M // micro_size_x,
+        block_N // micro_size_y,
+        micro_size_x,
+        micro_size_y,
+    )
+
+    warp_size = 32
+    threads = warp_size * (block_row_warps * block_col_warps)
+    local_size = (micro_size_x * micro_size_y) // warp_size
+    warp_rows = warp_row_tiles // micro_size_x
+    warp_cols = warp_col_tiles // micro_size_y
+
+    # MMA Wrapper to Auto Generate Code for MMA
+    mma_emitter = TensorCoreIntrinEmitterWithLadderTransform(
+        a_dtype=dtypeAB,
+        b_dtype=dtypeAB,
+        accum_dtype=accum_dtype,
+        a_transposed=False,
+        b_transposed=True,
+        block_row_warps=block_row_warps,
+        block_col_warps=block_col_warps,
+        warp_row_tiles=warp_row_tiles,
+        warp_col_tiles=warp_col_tiles,
+        chunk=chunk,
+        reduce_k=reduce_k,
+        transform_kind_b=transform_b,
+        num_elems_per_byte=num_elems_per_byte)
+
+    vec_load_qb = 16
+    if block_N * (block_K // reduce_k) // num_elems_per_byte // threads < vec_load_qb:
+        vec_load_qb = block_N * (block_K // reduce_k) // num_elems_per_byte // threads
+
+    @T.prim_func
+    def main(
+            A: T.Buffer(A_shape, dtypeAB),
+            B: T.Buffer(B_shape, storage_dtype),
+            C: T.Buffer((M, N), dtypeC),
+    ):
+        with T.Kernel(
+                T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads,
+                prelude=decode_i4_to_f16) as (bx, by):
+
+            A_shared = T.alloc_shared(A_shared_shape, dtypeAB, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, storage_dtype, scope=shared_scope)
+            C_shared = T.alloc_shared(C_shared_shape, dtypeC, scope=shared_scope)
+            A_local = T.alloc_local((warp_rows * local_size), dtypeAB)
+            B_local = T.alloc_local((warp_cols * local_size // num_elems_per_byte), storage_dtype)
+            B_dequantize_local = T.alloc_local((warp_cols * local_size), dtypeAB)
+            C_local = T.alloc_local((warp_rows * warp_cols * local_size), accum_dtype)
+            reduced_accum_res = T.alloc_local(0, accum_dtype)
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+            rk = T.thread_binding(0, reduce_k, "threadIdx.y")
+
+            T.annotate_layout({
+                A_shared: make_swizzle_layout(A_shared),
+            })
+
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined((K // block_K), num_stages=stage):
+
+                # Load A into shared memory
+                for i, k in T.Parallel(block_M, (block_K // reduce_k)):
+                    vk = rk * (block_K // reduce_k) + k
+                    A_shared[i, vk] = A[by * block_M + i, ko * block_K + vk]
+
+                # TODO(lei): Layout Inference Pass is not efficient to handle the four dims int8 load
+                for i in T.serial(block_N * (block_K // reduce_k) // num_elems_per_byte //
+                                  (threads * vec_load_qb)):
+                    for v in T.vectorized(0, vec_load_qb):
+                        t = thread_bindings
+                        idx = i * threads * vec_load_qb * reduce_k + rk * threads * vec_load_qb + t * vec_load_qb + v
+                        vkk = idx % (micro_size_k // num_elems_per_byte)
+                        vjj = (idx // (micro_size_k // num_elems_per_byte)) % micro_size_y
+                        vk = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y) % (
+                            block_K // micro_size_k)
+                        vj = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y //
+                              (block_K // micro_size_k)) % (
+                                  block_N // micro_size_y)
+                        B_shared[vj, vk, vjj,
+                                 vkk] = B[bx * (block_N // micro_size_y) + vj,
+                                          ko * (block_K // micro_size_k) + vk, vjj, vkk]
+
+                for ki in T.serial(0, (block_K // (micro_size_k * reduce_k))):
+
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_local,
+                        A_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                        rk=rk,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_local,
+                        B_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                        rk=rk,
+                    )
+
+                    for j in T.serial(warp_cols):
+                        local_size_b = mma_emitter.local_size_b
+                        T.call_extern('handle', 'decode_i4u_to_f16',
+                                      T.address_of(B_local[j * local_size_b // num_elems_per_byte]),
+                                      T.address_of(B_dequantize_local[j * local_size_b]), 8)
+
+                    mma_emitter.mma(A_local, B_dequantize_local, C_local)
+
+            if reduce_k > 1:
+                for n in T.serial(warp_rows * warp_cols * local_size):
+                    T.attr(
+                        T.comm_reducer(lambda x, y: x + y, [T.float16(0)]),
+                        "reduce_scope",
+                        T.reinterpret(T.uint64(0), dtype="handle"),
+                    )
+                    T.evaluate(
+                        T.tvm_thread_allreduce(
+                            T.uint32(1),
+                            C_local[n],
+                            True,
+                            reduced_accum_res[0],
+                            rk,
+                            dtype="handle",
+                        ))
+                    if rk == 0:
+                        C_local[n] = reduced_accum_res[0]
+
+            if rk == 0:
+                mma_emitter.stmatrix(
+                    C_local,
+                    C_shared,
+                    thread_bindings=thread_bindings,
+                )
+
+            for i, j in T.Parallel(block_M, (block_N // reduce_k)):
+                vj = rk * (block_N // reduce_k) + j
+                C[by * block_M + i,
+                  bx * block_N + vj] = C_shared[i // micro_size_x, vj // micro_size_y,
+                                                i % micro_size_x, vj % micro_size_y]
+
+    return main
+
+
+def assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correctness(
+    M,
+    N,
+    K,
+    in_dtype,
+    dtypeC,
+    accum_dtype,
+    transform_b,
+):
+    matmul = tl_matmul_with_ladder_weight_only_transform_block_reduce_int4(
+        M, N, K, in_dtype, dtypeC, accum_dtype, transform_b)
+
+    mod, params = TL.lower(matmul)
+    src_code = mod.imported_modules[0].get_source()
+
+    # src_code is the generated cuda source
+    assert src_code is not None
+    num_bits = 4
+    num_elems_per_byte = 8 // num_bits
+    storage_dtype = "int8"
+
+    A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
+    qB = torch.randint(
+        0, 127, (N, K // num_elems_per_byte), device="cuda", dtype=getattr(torch, storage_dtype))
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    ladder_permutate_config = bitblas.ops.LadderPermutateConfig(
+        M=N,
+        N=K,
+        transform_kind=transform_b,
+        transpose_matrix=True,
+        dequantize_bits=num_bits,
+        storage_dtype=storage_dtype,
+    )
+
+    ladder_permutate = bitblas.ops.LadderPermutate(ladder_permutate_config)
+
+    lop3_permutate_config = bitblas.ops.LOP3PermutateConfig(
+        M=N,
+        N=K,
+        datatype=in_dtype,
+        dequantize_bits=num_bits,
+        storage_dtype=storage_dtype,
+    )
+    lop3_permutate = bitblas.ops.LOP3Permutate(
+        config=lop3_permutate_config,
+        target=tvm.target.Target("llvm"),
+    )
+    QLB = ladder_permutate(qB.cpu()).cuda()
+    QLB = lop3_permutate(QLB.cpu()).cuda()
+
+    mod = TL.Profiler(mod, params, [], TL.TensorSupplyType.Integer)
+
+    mod(A, QLB, C)
+
+    latency = mod.do_bench(mod.func, warmup=25)
+
+    # Ensure that the latency is not None
+    assert latency is not None
+
+    B = (
+        torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4,
+                    dtype=torch.half).to(torch.half).to(A.device))
+    for i in range(B.shape[0]):
+        for j in range(B.shape[1]):
+            B[i][j] = ((qB[i][j // 2] >> (4 * (j % 2))) & 0xF).to(torch.half)
+
+    # Get Reference Result
+    ref_c = torch.matmul(A, B.T).to(getattr(torch, accum_dtype))
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
 def test_run_dequantize_gemm():
     run_gemm(256, 256, 256, "int8", "int32", "int32", 128, 128, 32, num_threads=128)
 
 
+def test_assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4():
+    assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correctness(
+        256, 1024, 512, "float16", "float16", "float16", 3)
+
+
 if __name__ == "__main__":
     bitblas.testing.main()