[MFMA][Test] Add scripts generating mfma related lit tests

This PR adds scripts for lit test generation.

scripts/amd/lit_tests/README.md

@@ -0,0 +1,21 @@
+# LIT test generator scripts
+
+### generate_accelerate_matmul_tests.pp
+
+This script generates CDNA related tests for AccelerateAMDMatmul pass.
+There are 3 generations of CDNA architecture, so to generate all tests following commands are needed:
+
+``` bash
+python3 generate_accelerate_matmul_tests.py 2 ../../../test/TritonGPU/accelerate-matmul-cdna1.mlir
+python3 generate_accelerate_matmul_tests.py 2 ../../../test/TritonGPU/accelerate-matmul-cdna2.mlir
+python3 generate_accelerate_matmul_tests.py 2 ../../../test/TritonGPU/accelerate-matmul-cdna3.mlir
+```
+
+### generate_mfma_variants.py
+
+This script generates CDNA related tests for TritonGPU to LLVM transformation:
+
+``` bash
+python3 generate_mfma_variants.py ../../../test/Conversion/AMDGPU/mfma_variants.mlir
+```
+

scripts/amd/lit_tests/generate_accelerate_matmul_tests.py

@@ -0,0 +1,164 @@
+import argparse
+import sys
+
+# M N K a_ty b_ty c_ty
+configs = [[32, 32, 32, "f16", "f16", "f32"],
+           [32, 32, 32, "bf16", "bf16", "f32"],
+           [32, 32, 32, "f32", "f32", "f32"],
+           [32, 32, 32, "i8", "i8", "i32"],
+           [32, 32, 32, "f8E4M3FNUZ", "f8E4M3FNUZ", "f32"],
+           [32, 32, 32, "f8E4M3FNUZ", "f8E5M2FNUZ", "f32"],
+           [32, 32, 32, "f8E5M2FNUZ", "f8E4M3FNUZ", "f32"],
+           [32, 32, 32, "f8E5M2FNUZ", "f8E5M2FNUZ", "f32"],
+
+           [16, 16, 32, "f16", "f16", "f32"],
+           [16, 16, 32, "bf16", "bf16", "f32"],
+           [16, 16, 32, "f32", "f32", "f32"],
+           [16, 16, 32, "i8", "i8", "i32"],
+           [16, 16, 32, "f8E4M3FNUZ", "f8E4M3FNUZ", "f32"],
+           [16, 16, 32, "f8E4M3FNUZ", "f8E5M2FNUZ", "f32"],
+           [16, 16, 32, "f8E5M2FNUZ", "f8E4M3FNUZ", "f32"],
+           [16, 16, 32, "f8E5M2FNUZ", "f8E5M2FNUZ", "f32"],
+
+           [4, 4, 64, "f16", "f16", "f32"],
+           [4, 4, 64, "bf16", "bf16", "f32"],
+           [4, 4, 64, "f32", "f32", "f32"],
+           [4, 4, 64, "i8", "i8", "i32"],
+           [4, 4, 64, "f8E4M3FNUZ", "f8E4M3FNUZ", "f32"],
+           [4, 4, 64, "f8E4M3FNUZ", "f8E5M2FNUZ", "f32"],
+           [4, 4, 64, "f8E5M2FNUZ", "f8E4M3FNUZ", "f32"],
+           [4, 4, 64, "f8E5M2FNUZ", "f8E5M2FNUZ", "f32"],
+
+           [64, 4, 4, "f16", "f16", "f32"],
+           [64, 4, 4, "bf16", "bf16", "f32"],
+           [64, 4, 4, "f32", "f32", "f32"],
+           [64, 4, 4, "i8", "i8", "i32"],
+           [64, 4, 4, "f8E4M3FNUZ", "f8E4M3FNUZ", "f32"],
+           [64, 4, 4, "f8E4M3FNUZ", "f8E5M2FNUZ", "f32"],
+           [64, 4, 4, "f8E5M2FNUZ", "f8E4M3FNUZ", "f32"],
+           [64, 4, 4, "f8E5M2FNUZ", "f8E5M2FNUZ", "f32"],
+
+           [4, 64, 4, "f16", "f16", "f32"],
+           [4, 64, 4, "bf16", "bf16", "f32"],
+           [4, 64, 4, "f32", "f32", "f32"],
+           [4, 64, 4, "i8", "i8", "i32"],
+           [4, 64, 4, "f8E4M3FNUZ", "f8E4M3FNUZ", "f32"],
+           [4, 64, 4, "f8E4M3FNUZ", "f8E5M2FNUZ", "f32"],
+           [4, 64, 4, "f8E5M2FNUZ", "f8E4M3FNUZ", "f32"],
+           [4, 64, 4, "f8E5M2FNUZ", "f8E5M2FNUZ", "f32"]
+          ]
+
+def generate(cdna_version, output_file):
+    arch_names = {0:"", 1: "gfx908", 2: "gfx90a", 3: "gfx940"}
+    arch_name = arch_names[cdna_version]
+    print(f"// This file is generated: $ python3 {' '.join(sys.argv)}", file=output_file)
+    print(f"// RUN: (! triton-opt %s -split-input-file --tritonamdgpu-accelerate-matmul=arch-generation-name={arch_name} --mlir-pass-pipeline-crash-reproducer=%t 2>/dev/null) | FileCheck --check-prefixes=CHECK %s", file=output_file)
+
+    for cfg_id in range(len(configs)):
+        cfg = configs[cfg_id]
+
+        cfg_name = "_".join([str(item) for item in cfg])
+
+        M, N, K, a_ty, b_ty, c_ty = cfg
+        if "i" in c_ty:
+            cst_val = "0"
+        else:
+            cst_val = "0.000000e+00"
+
+        supported = True
+        if cdna_version < 3 and ("f8" in a_ty or "f8" in b_ty):
+            supported = False
+
+        if M >= 32 and N >= 32:
+            m_dim = 32
+            n_dim = 32
+        elif M >= 16 and N >= 16:
+            m_dim = 16
+            n_dim = 16
+        elif M >= 64 and N < 16:
+            m_dim = 64
+            n_dim = 4
+        elif M < 16 and N >= 64:
+            m_dim = 4
+            n_dim = 64
+        elif M < 16 and N < 16:
+            m_dim = 4
+            n_dim = 4
+        if ("f8" in a_ty or "f8" in b_ty) and min(m_dim, n_dim) == 4:
+            supported = False
+
+        if cdna_version == 1:
+            if a_ty == "f16":
+                k_width = 4
+            if a_ty == "bf16":
+                k_width = 2
+            if a_ty == "i8":
+                k_width = 4
+            if a_ty == "f32":
+                k_width = 1
+        if cdna_version == 2:
+            if a_ty == "f16":
+                k_width = 4
+            if a_ty == "bf16":
+                k_width = 4
+            if a_ty == "i8":
+                k_width = 4
+            if a_ty == "f32":
+                k_width = 1
+        if cdna_version == 3:
+            if "f8" in a_ty:
+                k_width = 8
+            if a_ty == "f16":
+                k_width = 4
+            if a_ty == "bf16":
+                k_width = 4
+            if a_ty == "i8":
+                if min(m_dim, n_dim) == 4:
+                    k_width = 4
+                else:
+                    k_width = 8
+            if a_ty == "f32":
+                k_width = 1
+
+        if supported:
+            mfma_check = f"// CHECK: #mfma = #triton_gpu.mfma<{{versionMajor = {cdna_version}, versionMinor = 0, warpsPerCTA = [1, 1], instrShape = [{m_dim}, {n_dim}], isTransposed = false}}>"
+            label_check = f"// CHECK: convert_dot_{cfg_name}"
+            checks =f"""// CHECK: triton_gpu.convert_layout {{{{.*}}}} : (tensor<{{{{.*}}}}, #blocked>) -> tensor<{{{{.*}}}}, #mfma>
+// CHECK: triton_gpu.convert_layout {{{{.*}}}} : (tensor<{{{{.*}}}}, #triton_gpu.dot_op<{{opIdx = 0, parent = #blocked}}>>) -> tensor<{{{{.*}}}}, #triton_gpu.dot_op<{{opIdx = 0, parent = #mfma, kWidth = {k_width}}}>>
+// CHECK: triton_gpu.convert_layout {{{{.*}}}} : (tensor<{{{{.*}}}}, #triton_gpu.dot_op<{{opIdx = 1, parent = #blocked}}>>) -> tensor<{{{{.*}}}}, #triton_gpu.dot_op<{{opIdx = 1, parent = #mfma, kWidth = {k_width}}}>>"""
+        else:
+            mfma_check = ""
+            label_check = f"// CHECK-NOT: convert_dot_{cfg_name}"
+            checks = ""
+
+        case_text = f'''
+!a_ty = {a_ty}
+!b_ty = {b_ty}
+!c_ty = {c_ty}
+#blocked = #triton_gpu.blocked<{{sizePerThread = [1, 1], threadsPerWarp = [8, 8], warpsPerCTA = [1, 1], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [0, 1]}}>
+#dot_operand_a = #triton_gpu.dot_op<{{opIdx=0, parent=#blocked}}>
+#dot_operand_b = #triton_gpu.dot_op<{{opIdx=1, parent=#blocked}}>
+module attributes {{"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-warp" = 64 : i32}} {{
+{mfma_check}
+{label_check}
+    tt.func @convert_dot_{cfg_name}(%a: tensor<{M}x{K}x!a_ty, #dot_operand_a>, %b: tensor<{K}x{N}x!b_ty, #dot_operand_b>) -> tensor<{M}x{N}x!c_ty, #blocked> {{
+        %cst_c = arith.constant dense<{cst_val}> : tensor<{M}x{N}x!c_ty, #blocked>
+{checks}
+        %D = tt.dot %a, %b, %cst_c {{allowTF32 = true, maxNumImpreciseAcc = 0 : i32, transA = false, transB = false}} : tensor<{M}x{K}x!a_ty, #dot_operand_a> * tensor<{K}x{N}x!b_ty, #dot_operand_b> -> tensor<{M}x{N}x!c_ty, #blocked>
+        tt.return %D: tensor<{M}x{N}x!c_ty, #blocked>
+    }}
+}}
+
+'''
+        if cfg_id == len(configs) - 1:
+            print(case_text, end="", file=output_file)
+        else:
+            print(case_text, end="// -----\n", file=output_file)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("cdna_version", type=int)
+    parser.add_argument("output_file", type=str)
+    args = parser.parse_args()
+    with open(args.output_file, "w") as f:
+        generate(cdna_version=args.cdna_version, output_file=f)

scripts/amd/lit_tests/generate_mfma_variants.py

@@ -0,0 +1,135 @@
+import argparse
+import sys
+
+# matrix code version, mfma instruction name
+configs = [(3, "mfma_f32_32x32x16_fp8_fp8"),
+           (3, "mfma_f32_32x32x16_fp8_bf8"),
+           (3, "mfma_f32_32x32x16_bf8_fp8"),
+           (3, "mfma_f32_32x32x16_bf8_bf8"),
+           (2, "mfma_f32_32x32x8f16"),
+           (1, "mfma_f32_32x32x4bf16"),
+           (2, "mfma_f32_32x32x8bf16_1k"),
+           (2, "mfma_f32_32x32x2f32"),
+           (2, "mfma_i32_32x32x8i8"),
+           (3, "mfma_i32_32x32x16_i8"),
+           (3, "mfma_f32_16x16x32_fp8_fp8"),
+           (3, "mfma_f32_16x16x32_fp8_bf8"),
+           (3, "mfma_f32_16x16x32_bf8_fp8"),
+           (3, "mfma_f32_16x16x32_bf8_bf8"),
+           (2, "mfma_f32_16x16x16f16"),
+           (1, "mfma_f32_16x16x8bf16"),
+           (2, "mfma_f32_16x16x16bf16_1k"),
+           (2, "mfma_f32_16x16x4f32"),
+           (2, "mfma_i32_16x16x16i8"),
+           (3, "mfma_i32_16x16x32_i8"),
+           (2, "mfma_f32_4x4x4f16"),
+           (1, "mfma_f32_4x4x2bf16"),
+           (2, "mfma_f32_4x4x4bf16_1k"),
+           (2, "mfma_f32_4x4x1f32"),
+           (2, "mfma_i32_4x4x4i8")]
+
+def generate(output_file):
+    print(f'// This file is generated: $ python3 {" ".join(sys.argv)}', file=output_file)
+    print('// RUN: triton-opt %s -split-input-file --convert-triton-gpu-to-llvm="target=rocdl" 2>/dev/null | FileCheck --check-prefixes=CHECK,GCN %s', file=output_file)
+
+    for cfg_id in range(len(configs)):
+        matrix_core_version = configs[cfg_id][0]
+        cfg = configs[cfg_id][1]
+        parts = cfg.split("_")
+        if parts[-1] == "1k":
+            parts = parts[:-1]
+        shape = parts[2].split("x")
+        for ty in ["bf8", "f8", "bf16", "f16", "f32", "i8"]:
+            if ty in shape[-1]:
+              shape[-1] = shape[-1][:-len(ty)]
+              parts += [ty]
+              break
+        for i in range(len(shape)):
+            shape[i] = int(shape[i])
+# shape
+        non_k_dim = shape[0]
+        k_width = shape[2]
+        if non_k_dim == 32:
+            k_width //= 2
+        if non_k_dim == 16:
+            k_width //= 4
+# types
+        b_ty = parts[-1]
+        if b_ty in ["fp8", "bf8"]:
+            a_ty = parts[-2]
+        else:
+            a_ty = b_ty
+        c_ty = parts[1]
+
+        mlir_type_names = {
+            "fp8": "f8E4M3FNUZ",
+            "bf8": "f8E5M2FNUZ",
+            "f16": "f16",
+            "bf16": "bf16",
+            "f32": "f32",
+            "i8": "i8",
+            "i32": "i32"}
+        a_ty = mlir_type_names[a_ty]
+        b_ty = mlir_type_names[b_ty]
+        c_ty = mlir_type_names[c_ty]
+
+# misc
+        if "i" in c_ty:
+            cst_val = "0"
+        else:
+            cst_val = "0.000000e+00"
+
+# repeats
+        if non_k_dim == 32:
+            M = 128
+            N = 32
+            K = 256
+        if non_k_dim == 16:
+            M = 128
+            N = 32
+            K = 256
+        if non_k_dim == 4:
+            M = 128
+            N = 32
+            K = 256
+
+        num_subgroups = 1
+        if non_k_dim == 4:
+            num_subgroups = 16
+        num_reps = (M // non_k_dim) * (N // non_k_dim) * (K // (shape[2] * num_subgroups))
+
+# mlir operation name
+        cfg.split
+        mlir_op_name = "rocdl." + cfg.replace("_", ".")
+        case_text = f'''
+!a_ty = {a_ty}
+!b_ty = {b_ty}
+!c_ty = {c_ty}
+#k_width = {k_width}
+#non_k_dim = {non_k_dim}
+#mfmaVersion = {matrix_core_version}
+#mfma = #triton_gpu.mfma<{{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}}>
+#dot_operand_a = #triton_gpu.dot_op<{{opIdx=0, parent=#mfma, kWidth = #k_width}}>
+#dot_operand_b = #triton_gpu.dot_op<{{opIdx=1, parent=#mfma, kWidth = #k_width}}>
+module attributes {{"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32}} {{
+  // CHECK-LABEL: convert_dot_{cfg}
+  tt.func @convert_dot_{cfg}(%a: tensor<{M}x{K}x!a_ty, #dot_operand_a>, %b: tensor<{K}x{N}x!b_ty, #dot_operand_b>) {{
+    %cst_c = arith.constant dense<{cst_val}> : tensor<{M}x{N}x!c_ty, #mfma>
+    // GCN-COUNT-{num_reps}: {mlir_op_name}
+    %D = tt.dot %a, %b, %cst_c {{allowTF32 = true, maxNumImpreciseAcc = 0 : i32, transA = false, transB = false}} : tensor<{M}x{K}x!a_ty, #dot_operand_a> * tensor<{K}x{N}x!b_ty, #dot_operand_b> -> tensor<{M}x{N}x!c_ty, #mfma>
+    tt.return
+  }}
+}}
+
+'''
+        if cfg_id == len(configs) - 1:
+            print(case_text, end="", file=output_file)
+        else:
+            print(case_text, end="// -----\n", file=output_file)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("output_file", type=str)
+    args = parser.parse_args()
+    with open(args.output_file, "w") as f:
+        generate(output_file=f)

test/Conversion/AMDGPU/mfma_variants.mlir

@@ -1,3 +1,4 @@
+// This file is generated: $ python3 generate_mfma_variants.py ../../../test/Conversion/AMDGPU/mfma_variants.mlir
 // RUN: triton-opt %s -split-input-file --convert-triton-gpu-to-llvm="target=rocdl" 2>/dev/null | FileCheck --check-prefixes=CHECK,GCN %s
 
 !a_ty = f8E4M3FNUZ
@@ -90,7 +91,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 #k_width = 4
 #non_k_dim = 32
 #mfmaVersion = 2
-#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion , warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
+#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -111,7 +112,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 #k_width = 2
 #non_k_dim = 32
 #mfmaVersion = 1
-#mfma = #triton_gpu.mfma<{versionMajor = 1, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
+#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -132,7 +133,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 #k_width = 4
 #non_k_dim = 32
 #mfmaVersion = 2
-#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion , warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
+#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -153,7 +154,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 #k_width = 1
 #non_k_dim = 32
 #mfmaVersion = 2
-#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion , warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
+#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -174,7 +175,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 #k_width = 4
 #non_k_dim = 32
 #mfmaVersion = 2
-#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion , warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
+#mfma = #triton_gpu.mfma<{versionMajor = #mfmaVersion, warpsPerCTA=[1,1], instrShape = [#non_k_dim, #non_k_dim], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {

test/TritonGPU/accelerate-matmul-cdna1.mlir

@@ -1,3 +1,4 @@
+// This file is generated: $ python3 generate_accelerate_matmul_tests.py 1 ../../../test/TritonGPU/accelerate-matmul-cdna1.mlir
 // RUN: (! triton-opt %s -split-input-file --tritonamdgpu-accelerate-matmul=arch-generation-name=gfx908 --mlir-pass-pipeline-crash-reproducer=%t 2>/dev/null) | FileCheck --check-prefixes=CHECK %s
 
 !a_ty = f16

test/TritonGPU/accelerate-matmul-cdna2.mlir

@@ -1,3 +1,4 @@
+// This file is generated: $ python3 generate_accelerate_matmul_tests.py 2 ../../../test/TritonGPU/accelerate-matmul-cdna2.mlir
 // RUN: (! triton-opt %s -split-input-file --tritonamdgpu-accelerate-matmul=arch-generation-name=gfx90a --mlir-pass-pipeline-crash-reproducer=%t 2>/dev/null) | FileCheck --check-prefixes=CHECK %s
 
 !a_ty = f16

test/TritonGPU/accelerate-matmul-cdna3.mlir

@@ -1,3 +1,4 @@
+// This file is generated: $ python3 generate_accelerate_matmul_tests.py 3 ../../../test/TritonGPU/accelerate-matmul-cdna3.mlir
 // RUN: (! triton-opt %s -split-input-file --tritonamdgpu-accelerate-matmul=arch-generation-name=gfx940 --mlir-pass-pipeline-crash-reproducer=%t 2>/dev/null) | FileCheck --check-prefixes=CHECK %s
 
 !a_ty = f16