[TL][BugFix] Disable Buffer Vectorization and Add OP Related TL Test …

…Cases (#197)

* Refactor BatchMatMulEmitter and BatchMatMulSelector for improved readability and maintainability

* Refactor import statements for improved readability and maintainability

* Refactor import statements for improved readability and maintainability

* disable failure email for ci

* remove email notifications.

* move relax pass from testing to mlc_llm

* Refactor scripts with se check_eual_ref_scripts_with_emitter function

* Lint Fix

* Refactor scripts with se check_eual_ref_scripts_with_emitter function

* buf fix for matrix support

* lint fix

* dispatch tensor core based on shapes

* update install commands

* import scripts

* remove shared mem hack

* revert change for swizzling

* bug fix

* tl examples

* Enhance Swizzle

* lint fix

* test fix

* lint fix

* optimize layout

* update tl utils.

* macro optimization

* test fix

* gemm_ss

* doc fix

* lint fix

* lint fix

* remove debug print

* remove debug print

* vectorization init

* lint fix

* prelude update

* update tvm

* bug fix for reduce_k with shared memory

* bug fix

* bug fix

* Enhance Macro Generation

* Lift Layout to reduce load time

* lint fix

* test fix

* red fix

* tile lang macro example

* tile lang macro example

* optimize the marcro generator related items

* lint fix

* Tile Lang Test with Dynamic Symbolic

* more test case with block level programming

* all dynamic test case

* simplify the test case for dequantize gemm.

* dequant gemm updare.

* Tile Lang GEMM Implementation

* Tile Lang Gemm Fix

* Update subproject commit in 3rdparty/tvm
Refactor mma_layout.py and remove unused imports
Add matmul_macro_tensorcore and matmul_macro_tensorcore_weight_propagation_level_ldmatrix to dense/__init__.py
Refactor test_general_matmul_tilelang_impl.py to include additional matmul functions

* test fix

bitblas/ops/general_matmul/tilelang/dense/__init__.py

@@ -1,4 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from .matmul import matmul_blocked  # noqa: F401
+from .matmul import (
+    matmul_blocked,  # noqa: F401
+    matmul_macro_tensorcore,  # noqa: F401
+    matmul_macro_tensorcore_weight_propagation_level_ldmatrix  # noqa: F401
+)

bitblas/ops/general_matmul/tilelang/dense/matmul.py

@@ -1,25 +1,20 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 from bitblas import tvm as tvm
+from tvm import DataType
 import tvm.tl.language as T
 
 from bitblas.tl.utils import (
     get_mma_micro_size,
     make_swizzle_layout,
 )
 
-from bitblas.tl.macro_generator import (TensorCoreIntrinEmitter)
-
+from bitblas.tl.macro_generator import (
+    TensorCoreIntrinEmitter,
+    TensorCoreIntrinEmitterWithLadderTransform,
+)
 
-def maybe_pipeline(
-    iterable,
-    num_stages,
-):
-    enable_pipeline = num_stages > 1
-    if enable_pipeline:
-        return T.Pipelined(iterable, num_stages=num_stages)
-    else:
-        return T.serial(iterable)
+from bitblas.ops.operator import TransformKind
 
 
 def matmul_blocked(
@@ -59,7 +54,7 @@ def main(
                 T.use_swizzle(10)
 
             T.clear(C_local)
-            for k in maybe_pipeline(T.ceildiv(K, block_K), num_stages):
+            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
                 if trans_A:
                     T.copy(A[k * block_K, by * block_M], A_shared)
                 else:
@@ -80,6 +75,8 @@ def matmul_macro_tensorcore(
     K,
     dtypeAB,
     dtypeC,
+    trans_A,
+    trans_B,
     accum_dtype,
     block_row_warps,
     block_col_warps,
@@ -89,6 +86,8 @@ def matmul_macro_tensorcore(
     num_stages=2,
     enable_rasterization=False,
 ):
+    assert trans_A is False, "Currently only support Matrix A is not transposed"
+    assert trans_B is True, "Currently only support Matrix B is transposed"
 
     block_M = block_row_warps * warp_row_tiles
     block_N = block_col_warps * warp_col_tiles
@@ -129,9 +128,9 @@ def main(
     ):
         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, dtypeAB, shared_scope=shared_scope)
-            B_shared = T.alloc_shared(B_shared_shape, dtypeAB, shared_scope=shared_scope)
-            C_shared = T.alloc_shared(C_shared_shape, dtypeC, shared_scope=shared_scope)
+            A_shared = T.alloc_shared(A_shared_shape, dtypeAB, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, dtypeAB, 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), dtypeAB)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size), accum_dtype)
@@ -147,7 +146,7 @@ def main(
 
             T.clear(C_local)
 
-            for ko in maybe_pipeline(T.ceildiv(K, block_K), num_stages):
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
 
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -187,3 +186,132 @@ def main(
                                                i % micro_size_x, j % micro_size_y]
 
     return main
+
+
+def matmul_macro_tensorcore_weight_propagation_level_ldmatrix(
+    M,
+    N,
+    K,
+    dtypeAB,
+    dtypeC,
+    trans_A,
+    trans_B,
+    accum_dtype,
+    block_row_warps,
+    block_col_warps,
+    warp_row_tiles,
+    warp_col_tiles,
+    chunk,
+    num_stages=2,
+    enable_rasterization=False,
+):
+    assert trans_A is False, "Currently only support Matrix A is not transposed"
+    assert trans_B is True, "Currently only support Matrix B is transposed"
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = chunk
+
+    # TODO(lei): Can be generalized to analyzed from bank size
+    pad_factor = 8 if dtypeAB == "float16" else 16
+
+    can_swizzle_a = block_K * DataType(dtypeAB).bits == 512
+    apply_pad_a = not can_swizzle_a
+
+    micro_size_x, micro_size_y, micro_size_k = get_mma_micro_size(dtypeAB)
+
+    A_shape = (M, K)
+    B_shape = (N // micro_size_y, K // micro_size_k, micro_size_y, micro_size_k)
+    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)
+    C_shared_shape = (block_M // micro_size_x, block_N // micro_size_y, micro_size_x, micro_size_y)
+
+    warp_size = 32  # nvidia gpu warp size is 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
+
+    shared_scope = "shared.dyn"  # Literal["shared", "shared.dyn"] while shared for static shared memory
+    mma_emitter = TensorCoreIntrinEmitterWithLadderTransform(
+        a_dtype=dtypeAB,
+        b_dtype=dtypeAB,
+        accum_dtype=accum_dtype,
+        a_transposed=trans_A,
+        b_transposed=trans_B,
+        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,
+        transform_kind_b=TransformKind.LDMatrixTransform,
+    )
+
+    @T.prim_func
+    def main(
+            A: T.Buffer(A_shape, dtypeAB),
+            B: T.Buffer(B_shape, dtypeAB),
+            C: T.Buffer((M, N), dtypeC),
+    ):
+        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, dtypeAB, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, dtypeAB, 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), dtypeAB)
+            C_local = T.alloc_local((warp_rows * warp_cols * local_size), accum_dtype)
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+
+            T.annotate_layout({
+                A_shared: make_swizzle_layout(A_shared),
+                B_shared: make_swizzle_layout(B_shared),
+            })
+
+            if enable_rasterization:
+                T.use_swizzle(panel_size=10)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
+
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
+
+                for j, k, jj, kk in T.Parallel(block_N // micro_size_y, block_K // micro_size_k,
+                                               micro_size_y, micro_size_k):
+                    B_shared[j, k, jj, kk] = B[bx * (block_N // micro_size_y) + j,
+                                               ko * (block_K // micro_size_k) + k, jj, kk]
+
+                for ki in T.serial(0, (block_K // micro_size_k)):
+
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_local,
+                        A_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_local,
+                        B_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    mma_emitter.mma(A_local, B_local, C_local)
+
+            mma_emitter.stmatrix(
+                C_local,
+                C_shared,
+                thread_bindings=thread_bindings,
+            )
+
+            for i, j in T.Parallel(block_M, block_N):
+                C[by * block_M + i,
+                  bx * block_N + j] = C_shared[i // micro_size_x, j // micro_size_y,
+                                               i % micro_size_x, j % micro_size_y]
+
+    return main

bitblas/tl/mma_layout.py

@@ -1,8 +1,5 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-from tvm import arith
-from tvm import DataType
-from typing import Union, Literal
 
 
 def ldmatrix_32x8_to_shared_16x16_layout(thread_id, local_id):