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fixed c_buffer alloc

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This commit is contained in:
Jing Zhang
2021-12-02 05:03:03 +00:00
parent 4041850f11
commit d798c9b8c6
4 changed files with 10 additions and 10 deletions
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5
composable_kernel/include/tensor_operation/blockwise_gemm_xdlops.hpp
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@@ -38,7 +38,7 @@ struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1
static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
StaticBufferOfVectorTypeV2<AddressSpaceEnum_t::Vgpr,
vector_type<FloatAcc, 16>,
vector_type<FloatAcc, xdlops_gemm.GetRegSizePerXdlops()>,
MRepeat * NRepeat,
true>
c_thread_buf_;
@@ -136,7 +136,8 @@ struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1
constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
constexpr auto N = c_m0_m1_m2_n_tblk_lens[I3];
return make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I1, I1, M0, M1, M2, N));
return make_naive_tensor_descriptor_packed(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
}
__host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()

7
composable_kernel/include/tensor_operation/gridwise_gemm_xdlops_v2r3.hpp
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@@ -557,6 +557,9 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// output: register to global memory
{
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
@@ -569,10 +572,6 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
make_naive_tensor_descriptor_packed(make_tuple(
Number<M0>{}, Number<N0>{}, I1, I1, Number<M2>{}, I1, Number<M4>{}, I1));
// calculate origin of thread output tensor on global memory
// blockwise GEMM c matrix starting index
const auto c_thread_mtx_on_block =

6
composable_kernel/include/tensor_operation/xdlops_gemm.hpp
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@@ -507,7 +507,7 @@ struct MfmaSelector
static constexpr auto selected_mfma = mfma_type<GetMfma<base_type, MPerXdlops, NPerXdlops>()>{};
__host__ __device__ static constexpr void mfma_check()
__host__ __device__ constexpr MfmaSelector()
{
static_assert(selected_mfma.group_size * selected_mfma.num_groups_per_blk ==
selected_mfma.num_regs_per_blk,
@@ -533,8 +533,6 @@ struct MfmaSelector
"is_k_reduction wrong!");
}
__host__ __device__ constexpr MfmaSelector() { mfma_check(); }
static constexpr bool IsABroadcast()
{
static_assert(NPerXdlops >= MPerXdlops, "only support ABroadcast");
@@ -621,6 +619,8 @@ struct XdlopsGemm
return MPerXdlops * NPerXdlops / mfma_instr.wave_size;
}
__device__ static constexpr index_t GetWaveSize() { return mfma_instr.wave_size; }
template <class FloatA, class FloatB, class FloatC>
__device__ void Run(const FloatA& p_a_wave, const FloatB& p_b_wave, FloatC& p_c_thread) const
{

2
host/driver_offline/src/conv_fwd_driver_offline.cpp
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@@ -18,7 +18,7 @@
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 0
#define USE_DYNAMIC_MODE 1
#define USE_CONV_FWD_V4R4_NCHW 0
#define USE_CONV_FWD_V4R4R2_NHWC 0
#define USE_CONV_FWD_V6R1_NCHW 0