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>
2025-12-15 18:27:08 +00:00 · 2024-10-17 12:59:52 -06:00
parent 05cd82ef94
commit f2890a4494
263 changed files with 14255 additions and 10867 deletions
--- a/llama/ggml-cuda/binbcast.cu
+++ b/llama/ggml-cuda/binbcast.cu
@@ -1,5 +1,5 @@
 /**
- * llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
+ * llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
 *
 * MIT License
 *
@@ -25,6 +25,7 @@
 */

 #include "binbcast.cuh"
+#include <cstdint>

 static __device__ __forceinline__ float op_repeat(const float a, const float b) {
    return b;
@@ -116,6 +117,30 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
 }

+template <typename T>
+static __global__ void k_repeat_back(
+    const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
+    const int64_t ne0, const int64_t ne1, const int64_t ne2) {
+
+    const int64_t tid0 = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
+    const int64_t tid1 = (int64_t) blockIdx.y*blockDim.y + threadIdx.y;
+    const int64_t tid2 = (int64_t) blockIdx.z*blockDim.z + threadIdx.z;
+
+    if (tid0 >= ne0) {
+        return;
+    }
+
+    T sum = 0;
+    for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
+        for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
+            for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
+                sum += src[i2*ne01*ne00 + i1*ne00 + i0];
+            }
+        }
+    }
+    dst[tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
+}
+
 template<float (*bin_op)(const float, const float)>
 struct bin_bcast_cuda {
    template<typename src0_t, typename src1_t, typename dst_t>
@@ -273,6 +298,16 @@ struct bin_bcast_cuda {
    }
 };

+template <typename T>
+static void repeat_back_cuda(
+    const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
+    const int64_t ne0, const int64_t ne1, const int64_t ne2, cudaStream_t stream) {
+
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2);
+    k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>(src, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+}
+
 template<class op>
 static void ggml_cuda_op_bin_bcast(
    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
@@ -312,3 +347,35 @@ void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
 }
+
+void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_can_repeat(dst, src0));
+
+    cudaStream_t stream = ctx.stream();
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    GGML_ASSERT(src0->ne[3] == 1);
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    GGML_ASSERT(dst->ne[3] == 1);
+
+    switch (dst->type) {
+        case GGML_TYPE_F32: {
+            const float * src0_d = (const float *) src0->data;
+            float       * dst_d  = (float       *) dst->data;
+            repeat_back_cuda<float>(src0_d, dst_d, ne00, ne01, ne02, ne0, ne1, ne2, stream);
+        } break;
+        default: {
+            GGML_ASSERT(false);
+        } break;
+    }
+}