[Mlir-commits] [mlir] [mlir] GEMM Hopper Tensor Core Integration Test (PR #81478)

[Manish Gupta]

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+import numpy as np
+from mlir import ir
+from mlir.dialects import arith
+from mlir.dialects import func
+from mlir.dialects import gpu
+from mlir.dialects import memref
+from mlir.dialects import nvgpu
+from mlir.dialects import nvvm
+from mlir.dialects import llvm
+from mlir.dialects import builtin
+from mlir.dialects import scf
+from mlir.dialects import vector
+
+TMA_LAST_DIM_F16 = 64  # 128B flaot16
+WARP_SIZE = 32
+WARP_GROUP_SIZE = WARP_SIZE * 4
+
+PRODUCER_REGISTER_SIZE = 40
+CONSUMER_REGISTER_SIZE = 232
+
+PRODUCER_PRIMARY_THREAD = 128
+CONSUMER_PRIMARY_THREAD = 0
+
+MLIR_DYNAMIC = -9223372036854775808
+f16_byte = 2
+f32_byte = 4
+
+DEBUG = False
+
+
+def debug_print(fmt, *args, predicate=None, threadNumber=-1, forcePrint=False):
+    if not DEBUG and not forcePrint:
+        return
+    type_formats = []
+    for arg in args:
+        ty_format = None
+        if ir.IndexType.isinstance(arg.type):
+            ty_format = "%llu"
+        if ir.IntegerType.isinstance(arg.type):
+            width = ir.IntegerType(arg.type).width
+            if width == 64:
+                ty_format = "%llu"
+            elif width == 32:
+                ty_format = "%d"
+            elif width == 1:
+                ty_format = "%i"
+        if ir.F32Type.isinstance(arg.type):
+            ty_format = "%f"
+        if ty_format is None:
+            raise NotImplementedError(arg.type)
+        type_formats.append(ty_format)
+    if threadNumber != -1:
+        tidx = gpu.thread_id(gpu.Dimension.x)
+        predicate = arith.cmpi(arith.CmpIPredicate.eq, tidx, c(threadNumber))
+        scf.yield_([])
+    if_op = scf.IfOp(predicate)
+    with ir.InsertionPoint(if_op.then_block):
+        gpu.printf(fmt.format(*type_formats) + "\n", args)
+        scf.yield_([])
+
+
+def c(value, ty=None):
+    ty = ir.IndexType.get() if ty is None else ty
+    return arith.constant(ty, value)
+
+
+def generate_matmul_ws(
+    input_type=np.float16,
+    output_type=np.float32,
+    M=4096,
+    N=4096,
+    K=4096,
+    BLOCK_M=128,
+    BLOCK_N=128,
+    BLOCK_K=128,
+    max_num_stages=3,
+):
+    # Limitaitons for now
+    assert input_type == np.float16
+    assert output_type == np.float32
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manishucsd wrote:

ok. I see that, even though we have these come in as arguments to the function `generate_matmul_ws`, but we are generating it for a fixed datatype. 

https://github.com/llvm/llvm-project/pull/81478