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https://github.com/dogkeeper886/ollama37.git
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IBM granite/granitemoe architecture support (#6760)
* fix(ext_server): Port llama.cpp sampling refactors to ext_server
This was a fairly large changeset. I closely followed the changes here:
df270ef745
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(server.cpp): Refactor server.cpp logging for llama.cpp overhaul
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Bump llama.cpp to the latest master with `granite` support
This does not yet have granite MoE support, but that can come in a
follow up PR
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches (except solar-pro) to work with bumped llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update solar patch for llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update the solar-pro patch for latest llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump to the latest master of llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches for latest bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama): Always run sync.sh from the right directory
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Update llama patches
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama)!: Rough sync with llama.cpp submodule
There are a number of changes that will need to be propagated to llama.go
before any of this works!
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Add a patch and update for missing ggml-impl.h include
This include is where the ggml_cgraph struct is defined. It is included in
many of the .c files to define the forward declartion in ggml.h. It seems
that with the subset of code included here, the import was somehow lost (or
out-of-order) when building, so adding this include to llama.cpp fixes the
missing definition.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/sync): Add missing ggml-cpu-impl.h copy-over in sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Add missing log.cpp
This was added as part of the logging overhaul done in llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Overhaul use of sampling module for llama.cpp changes
The changes here reflect the changes made in the big llama.cpp sampling PR
https://github.com/ggerganov/llama.cpp/pull/9294
The sampling functionality is now broken into the base interface
(llama_sampler) and the generation implementation (gpt_sampler). The
changes here reflect that. Since the sampling.h/sampling.cpp code uses c++
STL headers, the sampling_ext.[h|cpp] wrapper is maintained to allow go to
access a pure-C interface.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix the impl of SampleTokenGreedy for new sampling
I don't think this method is currently used, so it could probably just be
removed so that all sampling goes through the GPT interface, but in the
interest of doing no harm, this should keep the method working as expected.
Branch: IBMGraniteArchitectureSupport
* fix(llama): Remove unused SampleTokenGreedy
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(sync): Remove bash-specific change to sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* chore(gofumpt): Format on llama.go to pass linting
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Fix missing <thread> include in ext_server
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove TODO about grammar_first
This feature was not used/needed previously so should be fine without
plumbing it through now.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Better naming for sampling wrapper and args
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix patch 05 to use new wrapper api and re-sync
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* runner: Flush pending responses before returning
If there are any pending reponses (such as from potential stop
tokens) then we should send them back before ending the sequence.
Otherwise, we can be missing tokens at the end of a response.
Fixes #6707
* fix(llama/sampling): Use gpt_sampler with a forward declaration
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove unnecessary patch for gguf impl header
This was caused by an earlier mistake in the embeddings patch that was
dereferencing the pointer instead of using the wrapper API.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Remove use of deprecated --log-disable flag
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
88
llama/ggml-cuda/cpy.cu
vendored
88
llama/ggml-cuda/cpy.cu
vendored
@@ -1,5 +1,5 @@
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/**
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* llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
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* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
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*
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* MIT License
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*
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@@ -107,6 +107,17 @@ static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
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}
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}
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static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
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const block_q8_0 * xi = (const block_q8_0 *) cxi;
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float * dsti = (float *) cdsti;
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const float d = (float)xi->d;
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for (int j = 0; j < QK8_0; j++) {
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dsti[j] = xi->qs[j] * d;
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}
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}
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static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q4_0 * dsti = (block_q4_0 *) cdsti;
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@@ -314,6 +325,32 @@ static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
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cpy_blck(cx + x_offset, cdst + dst_offset);
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}
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template <cpy_kernel_t cpy_blck, int qk>
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static __global__ void cpy_q_f32(const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
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const int nb12, const int nb13) {
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const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
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if (i >= ne) {
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return;
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}
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const int i03 = i/(ne00 * ne01 * ne02);
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const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
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const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
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const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
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const int x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
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const int i13 = i/(ne10 * ne11 * ne12);
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const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
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const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
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const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
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const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
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cpy_blck(cx + x_offset, cdst + dst_offset);
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}
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static void ggml_cpy_f16_f32_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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@@ -355,6 +392,16 @@ static void ggml_cpy_f32_q8_0_cuda(
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_q8_0_f32_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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const int num_blocks = ne;
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cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_q4_0_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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@@ -454,12 +501,17 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
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char * src0_ddc = (char *) src0->data;
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char * src1_ddc = (char *) src1->data;
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if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
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if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
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GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
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CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
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ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
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ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
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ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
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ggml_cpy_q8_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
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ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
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@@ -475,9 +527,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
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} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
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ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
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} else {
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fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
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GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
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ggml_type_name(src0->type), ggml_type_name(src1->type));
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GGML_ABORT("fatal error");
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}
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}
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@@ -487,29 +538,32 @@ void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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}
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void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
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if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
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return (void*) cpy_f32_f16<cpy_1_f32_f32>;
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if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
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return nullptr;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
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return (void*) cpy_f32_f16<cpy_1_f32_f32>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
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return (void*) cpy_f32_f16<cpy_1_f32_f16>;
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return (void*) cpy_f32_f16<cpy_1_f32_f16>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
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return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
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return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
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} else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
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return (void*) cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
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return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
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return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
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return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
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return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
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return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
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return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
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return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
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return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
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} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
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return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
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return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
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} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
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return (void*) cpy_f32_f16<cpy_1_f32_f16>;
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return (void*) cpy_f32_f16<cpy_1_f32_f16>;
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} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
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return (void*) cpy_f32_f16<cpy_1_f16_f32>;
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return (void*) cpy_f32_f16<cpy_1_f16_f32>;
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} else {
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fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
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GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
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ggml_type_name(src0->type), ggml_type_name(src1->type));
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GGML_ABORT("fatal error");
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}
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}
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