sunggg commented on code in PR #15111:
URL: https://github.com/apache/tvm/pull/15111#discussion_r1232777164
##########
tests/python/relax/test_codegen_cutlass.py:
##########
@@ -1250,5 +1250,243 @@ def main(
tvm.ir.assert_structural_equal(mod, Expected)
+def test_fp16A_int4B_gemm():
+ @I.ir_module
+ class Module:
+ @T.prim_func
+ def decode(
+ A: T.Buffer((T.int64(64), T.int64(64)), "int8"),
+ B: T.Buffer((T.int64(128),), "float16"),
+ decode_1: T.Buffer((T.int64(64), T.int64(128)), "float16"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ # with T.block("root"):
+ for i, j in T.grid(T.int64(64), T.int64(128)):
+ with T.block("decode"):
+ v_i, v_j = T.axis.remap("SS", [i, j])
+ T.reads(A[v_i, v_j // T.int64(2)], B[v_j])
+ T.writes(decode_1[v_i, v_j])
+ decode_1[v_i, v_j] = (
+ T.Cast(
+ "float16",
+ T.shift_right(
+ T.shift_left(
+ T.bitwise_and(
+ T.shift_right(
+ T.Cast("int32", A[v_i, v_j //
T.int64(2)]),
+ T.Cast("int32", v_j % T.int64(2))
* 4,
+ ),
+ 15,
+ ),
+ 28,
+ ),
+ 28,
+ ),
+ )
+ * B[v_j]
+ )
+
+ @T.prim_func
+ def encode(
+ A: T.Buffer((T.int64(128), T.int64(64)), "float16"),
+ w_gathered: T.Buffer((T.int64(64), T.int64(64)), "int8"),
+ compute: T.Buffer((T.int64(128),), "float16"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ # with T.block("root"):
+ max_abs_value = T.alloc_buffer((T.int64(128),), "float16")
+ scale = T.alloc_buffer((T.int64(128),))
+ for i, k in T.grid(T.int64(128), T.int64(64)):
+ with T.block("max_abs_value"):
+ v_i, v_k = T.axis.remap("SR", [i, k])
+ T.reads(A[v_i, v_k])
+ T.writes(max_abs_value[v_i])
+ with T.init():
+ max_abs_value[v_i] = T.float16(-65504)
+ max_abs_value[v_i] = T.max(max_abs_value[v_i],
T.fabs(A[v_i, v_k]))
+ for i in range(T.int64(128)):
+ with T.block("scale"):
+ v_i = T.axis.spatial(T.int64(128), i)
+ T.reads(max_abs_value[v_i])
+ T.writes(scale[v_i])
+ scale[v_i] = T.max(
+ T.Cast("float32", max_abs_value[v_i]),
T.float32(0.0001)
+ ) * T.float32(0.125)
+ for j, i, k in T.grid(T.int64(64), T.int64(64), T.int64(2)):
+ with T.block("w_gathered"):
+ v_j, v_i, v_k = T.axis.remap("SSR", [j, i, k])
+ T.reads(A[v_i * T.int64(2) + v_k, v_j], scale[v_i *
T.int64(2) + v_k])
+ T.writes(w_gathered[v_j, v_i])
+ with T.init():
+ w_gathered[v_j, v_i] = T.int8(0)
+ w_gathered[v_j, v_i] = T.bitwise_or(
+ w_gathered[v_j, v_i],
+ T.if_then_else(
+ v_i * T.int64(2) + v_k < T.int64(128),
+ T.shift_left(
+ T.bitwise_and(
+ T.Cast(
+ "int8",
+ T.min(
+ T.max(
+ T.round(
+ T.Cast(
+ "float32", A[v_i *
T.int64(2) + v_k, v_j]
+ )
+ / scale[v_i * T.int64(2) +
v_k]
+ ),
+ T.float32(-8),
+ ),
+ T.float32(7),
+ ),
+ ),
+ T.int8(15),
+ ),
+ T.Cast("int8", v_k) * T.int8(4),
+ ),
+ T.int8(0),
+ ),
+ )
+ for i0 in range(T.int64(128)):
+ with T.block("compute"):
+ v_i0 = T.axis.spatial(T.int64(128), i0)
+ T.reads(scale[v_i0])
+ T.writes(compute[v_i0])
+ compute[v_i0] = T.Cast("float16", scale[v_i0])
+
+ @R.function
+ def main_bias(
+ x: R.Tensor((64, 64), dtype="float16"),
+ y: R.Tensor((128, 64), dtype="float16"),
+ bias: R.Tensor((1, 128), dtype="float16"),
+ ) -> R.Tensor((64, 128), dtype="float16"):
+ R.func_attr({"num_input": 1})
+ cls = Module
+ with R.dataflow():
+ lv = R.call_tir(
+ cls.encode,
+ (y,),
+ out_sinfo=[R.Tensor((64, 64), dtype="int8"),
R.Tensor((128,), dtype="float16")],
+ )
+ lv1 = lv[0]
+ lv2 = R.call_pure_packed(
+ "cutlass.ft_preprocess_weight_int4",
+ lv1,
+ 80,
+ sinfo_args=(R.Tensor((64, 64), dtype="int8"),),
+ )
+ lv3: R.Tensor((128,), dtype="float16") = lv[1]
+ lv6 = R.call_tir(
Review Comment:
I'm wondering, in the future, once we settle down representative
quantization schemes, would it make sense to introduce relax-level decode op?
##########
src/runtime/contrib/cutlass/weight_preprocess.cc:
##########
@@ -0,0 +1,46 @@
+/*
+ * 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.
+ */
+
+#include <tvm/runtime/ndarray.h>
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/registry.h>
+
+#include
"../../../3rdparty/cutlass_fpA_intB_gemm/cutlass_kernels/cutlass_preprocessors.h"
+
+namespace tvm {
+namespace runtime {
+
+TVM_REGISTER_GLOBAL("cutlass.ft_preprocess_weight_int4")
+ .set_body_typed([](NDArray packed_weight, int sm) {
+ int rows = packed_weight->shape[0];
+ int cols = packed_weight->shape[1];
+ std::vector<int8_t> input_cpu(rows * cols);
+ std::vector<int8_t> output_cpu(rows * cols);
+ packed_weight.CopyToBytes(input_cpu.data(), input_cpu.size());
+ // multiply cols by 2 since the "col" params in preprocess_weights
refers to the column of
+ // the unpacked weight.
+ fastertransformer::preprocess_weights(output_cpu.data(),
input_cpu.data(), rows, cols * 2,
Review Comment:
Maybe good to add some documentation about why we need this preprocess, why
it is happening on CPU, and if it is possible/better to move it on GPU?
##########
tests/python/relax/test_codegen_cutlass.py:
##########
@@ -1250,5 +1250,243 @@ def main(
tvm.ir.assert_structural_equal(mod, Expected)
+def test_fp16A_int4B_gemm():
+ @I.ir_module
+ class Module:
+ @T.prim_func
+ def decode(
+ A: T.Buffer((T.int64(64), T.int64(64)), "int8"),
+ B: T.Buffer((T.int64(128),), "float16"),
+ decode_1: T.Buffer((T.int64(64), T.int64(128)), "float16"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ # with T.block("root"):
+ for i, j in T.grid(T.int64(64), T.int64(128)):
+ with T.block("decode"):
+ v_i, v_j = T.axis.remap("SS", [i, j])
+ T.reads(A[v_i, v_j // T.int64(2)], B[v_j])
+ T.writes(decode_1[v_i, v_j])
+ decode_1[v_i, v_j] = (
+ T.Cast(
+ "float16",
+ T.shift_right(
+ T.shift_left(
+ T.bitwise_and(
+ T.shift_right(
+ T.Cast("int32", A[v_i, v_j //
T.int64(2)]),
+ T.Cast("int32", v_j % T.int64(2))
* 4,
+ ),
+ 15,
+ ),
+ 28,
+ ),
+ 28,
+ ),
+ )
+ * B[v_j]
+ )
+
+ @T.prim_func
+ def encode(
+ A: T.Buffer((T.int64(128), T.int64(64)), "float16"),
+ w_gathered: T.Buffer((T.int64(64), T.int64(64)), "int8"),
+ compute: T.Buffer((T.int64(128),), "float16"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ # with T.block("root"):
+ max_abs_value = T.alloc_buffer((T.int64(128),), "float16")
+ scale = T.alloc_buffer((T.int64(128),))
+ for i, k in T.grid(T.int64(128), T.int64(64)):
+ with T.block("max_abs_value"):
+ v_i, v_k = T.axis.remap("SR", [i, k])
+ T.reads(A[v_i, v_k])
+ T.writes(max_abs_value[v_i])
+ with T.init():
+ max_abs_value[v_i] = T.float16(-65504)
+ max_abs_value[v_i] = T.max(max_abs_value[v_i],
T.fabs(A[v_i, v_k]))
+ for i in range(T.int64(128)):
+ with T.block("scale"):
+ v_i = T.axis.spatial(T.int64(128), i)
+ T.reads(max_abs_value[v_i])
+ T.writes(scale[v_i])
+ scale[v_i] = T.max(
+ T.Cast("float32", max_abs_value[v_i]),
T.float32(0.0001)
+ ) * T.float32(0.125)
+ for j, i, k in T.grid(T.int64(64), T.int64(64), T.int64(2)):
+ with T.block("w_gathered"):
+ v_j, v_i, v_k = T.axis.remap("SSR", [j, i, k])
+ T.reads(A[v_i * T.int64(2) + v_k, v_j], scale[v_i *
T.int64(2) + v_k])
+ T.writes(w_gathered[v_j, v_i])
+ with T.init():
+ w_gathered[v_j, v_i] = T.int8(0)
+ w_gathered[v_j, v_i] = T.bitwise_or(
+ w_gathered[v_j, v_i],
+ T.if_then_else(
+ v_i * T.int64(2) + v_k < T.int64(128),
+ T.shift_left(
+ T.bitwise_and(
+ T.Cast(
+ "int8",
+ T.min(
+ T.max(
+ T.round(
+ T.Cast(
+ "float32", A[v_i *
T.int64(2) + v_k, v_j]
+ )
+ / scale[v_i * T.int64(2) +
v_k]
+ ),
+ T.float32(-8),
+ ),
+ T.float32(7),
+ ),
+ ),
+ T.int8(15),
+ ),
+ T.Cast("int8", v_k) * T.int8(4),
+ ),
+ T.int8(0),
+ ),
+ )
+ for i0 in range(T.int64(128)):
+ with T.block("compute"):
+ v_i0 = T.axis.spatial(T.int64(128), i0)
+ T.reads(scale[v_i0])
+ T.writes(compute[v_i0])
+ compute[v_i0] = T.Cast("float16", scale[v_i0])
+
+ @R.function
+ def main_bias(
+ x: R.Tensor((64, 64), dtype="float16"),
+ y: R.Tensor((128, 64), dtype="float16"),
+ bias: R.Tensor((1, 128), dtype="float16"),
+ ) -> R.Tensor((64, 128), dtype="float16"):
+ R.func_attr({"num_input": 1})
+ cls = Module
+ with R.dataflow():
+ lv = R.call_tir(
+ cls.encode,
+ (y,),
+ out_sinfo=[R.Tensor((64, 64), dtype="int8"),
R.Tensor((128,), dtype="float16")],
+ )
+ lv1 = lv[0]
+ lv2 = R.call_pure_packed(
+ "cutlass.ft_preprocess_weight_int4",
+ lv1,
+ 80,
+ sinfo_args=(R.Tensor((64, 64), dtype="int8"),),
+ )
+ lv3: R.Tensor((128,), dtype="float16") = lv[1]
+ lv6 = R.call_tir(
+ cls.decode, (lv2, lv3), out_sinfo=R.Tensor((64, 128),
dtype="float16")
+ )
+ lv1_1: R.Tensor((64, 128), dtype="float16") = R.matmul(x, lv6,
out_dtype="float16")
+ lv2_1: R.Tensor((64, 128), dtype="float16") = R.add(lv1_1,
bias)
+ R.output(lv2_1)
+ return lv2_1
+
+ @R.function
+ def main_residual(
+ x: R.Tensor((64, 64), dtype="float16"),
+ residual: R.Tensor((64, 128), dtype="float16"),
+ y: R.Tensor((128, 64), dtype="float16"),
+ bias: R.Tensor((1, 128), dtype="float16"),
+ ) -> R.Tensor((64, 128), dtype="float16"):
+ R.func_attr({"num_input": 2})
+ cls = Module
+ with R.dataflow():
+ lv = R.call_tir(
+ cls.encode,
+ (y,),
+ out_sinfo=[R.Tensor((64, 64), dtype="int8"),
R.Tensor((128,), dtype="float16")],
+ )
+ lv1 = lv[0]
+ lv2 = R.call_pure_packed(
+ "cutlass.ft_preprocess_weight_int4",
Review Comment:
If we always have to apply this preprocess, can we do the preprocess and
then dump the weight?
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