cuda : loading models directly into VRAM, norm calculation on GPU, broadcasting for ggml_mul (#1483)

* Broadcasting for ggml_mul

* CUDA kernel for ggml_mul, norms in VRAM

* GPU weights not in RAM, direct loading with cuFile

* fixup! GPU weights not in RAM, direct loading with cuFile

* fixup! GPU weights not in RAM, direct loading with cuFile

* define default model path once, sync path with readme (#1366)

* ~7% faster Q5_1 AVX2 code (#1477)

* convert.py: Support models which are stored in a single pytorch_model.bin (#1469)

* Support models in a single pytorch_model.bin

* Remove spurious line with typo

* benchmark-matmul: Print the average of the test results (#1490)

* Remove unused n_parts parameter (#1509)

* Fixes #1511 lambda issue for w64devkit (mingw) (#1513)

* Fix for w64devkit and mingw

* make kv_f16 the default for api users (#1517)

* minor : fix compile warnings

* readme : adds WizardLM to the list of supported models (#1485)

* main : make reverse prompt option act as a stop token in non-interactive mode (#1032)

* Make reverse prompt option act as a stop token in non-interactive scenarios

* Making requested review changes

* Update gpt_params_parse and fix a merge error

* Revert "Update gpt_params_parse and fix a merge error"

This reverts commit 2bb2ff1748513591ad45b175a75ed1d8089d84c8.

* Update gpt_params_parse and fix a merge error take 2

* examples : add persistent chat (#1495)

* examples : add persistent chat

* examples : fix whitespace

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* tests : add missing header

* ggml : use F16 instead of F32 in Q4_0, Q4_1, Q8_0 (#1508)

* ggml : use F16 instead of F32 in Q4_0, Q4_1 and Q8_0

* llama : bump LLAMA_FILE_VERSION to 3

* cuda : update Q4 and Q8 dequantize kernels

* ggml : fix AVX dot products

* readme : update performance table + hot topics

* ggml : fix scalar implementation of Q4_1 dot

* llama : fix compile warnings in llama_set_state_data()

* llama : fix name shadowing and C4146 (#1526)

* Fix name shadowing and C4146

* Fix if macros not using defined when required

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Code style

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Fix for mingw (#1462)

* llama : add llama_init_backend() API (close #1527)

* feature : add blis and other BLAS implementation support (#1502)

* feature: add blis support

* feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927

* fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake

* Fix typo in INTEGER

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Revert "feature : add blis and other BLAS implementation support (#1502)"

This reverts commit 07e9ace0f9da424d82e75df969642522880feb92.

* GPU weights not in RAM, direct loading with cuFile

* llama : code style fixes + progress print fix

* ggml : ggml_mul better broadcast support

* cmake : workarounds for cufile when CMake version < 3.25

* gg rebase fixup

* Loop in llama.cpp, fixed progress callback

* Attempt clang-tidy fix

* llama : fix vram size computation

* Add forgotten fclose()

---------

Co-authored-by: András Salamon <ott2@users.noreply.github.com>
Co-authored-by: Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
Co-authored-by: Tom Jobbins <784313+TheBloke@users.noreply.github.com>
Co-authored-by: rankaiyx <rankaiyx@rankaiyx.com>
Co-authored-by: Stephan Walter <stephan@walter.name>
Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com>
Co-authored-by: Erik Scholz <Green-Sky@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: David Kennedy <dakennedyd@gmail.com>
Co-authored-by: Jason McCartney <jmac@theroot.org>
Co-authored-by: Evan Jones <evan.q.jones@gmail.com>
Co-authored-by: Maxime <672982+maximegmd@users.noreply.github.com>
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1 files changed, 62 insertions, 30 deletions

diff --git a/ggml.c b/ggml.c
index 939ab4d6..d86e5942 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3776,6 +3776,12 @@ static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct g
         (t1->ne[3]%t0->ne[3] == 0);
 }
 
+static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1);
+}
+
 static inline int ggml_up32(int n) {
     return (n + 31) & ~31;
 }
@@ -4658,11 +4664,15 @@ struct ggml_tensor * ggml_mul_impl(
         struct ggml_tensor * a,
         struct ggml_tensor * b,
         bool inplace) {
-    GGML_ASSERT(ggml_are_same_shape(a, b));
+    // TODO: support less-strict constraint
+    //       GGML_ASSERT(ggml_can_repeat(b, a));
+    GGML_ASSERT(ggml_can_repeat_rows(b, a));
 
     bool is_node = false;
 
     if (!inplace && (a->grad || b->grad)) {
+        // TODO: support backward pass for broadcasting
+        GGML_ASSERT(ggml_are_same_shape(a, b));
         is_node = true;
     }
 
@@ -7960,7 +7970,7 @@ static void ggml_compute_forward_mul_f32(
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
-    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
@@ -7968,10 +7978,25 @@ static void ggml_compute_forward_mul_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
+#ifdef GGML_USE_CUBLAS
+    if (src1->backend == GGML_BACKEND_CUDA) {
+        if (ith == 0) {
+            ggml_cuda_mul(src0, src1, dst);
+        }
+        return;
+    }
+#endif
+
+    const int64_t nr = ggml_nrows(src0);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
 
     const size_t nb00 = src0->nb[0];
     const size_t nb01 = src0->nb[1];
@@ -7990,44 +8015,51 @@ static void ggml_compute_forward_mul_f32(
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(ne00 == ne10);
 
     if (nb10 == sizeof(float)) {
-        for (int ir = ith; ir < nr; ir += nth) {
-            // src0, src1 and dst are same shape => same indices
-            const int i3 = ir/(ne2*ne1);
-            const int i2 = (ir - i3*ne2*ne1)/ne1;
-            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
 
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
 
 #ifdef GGML_USE_ACCELERATE
             UNUSED(ggml_vec_mul_f32);
 
-            vDSP_vmul(
-                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
-                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
-                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ), 1,
-                    ne0);
+            vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr,  1, ne00);
 #else
-            ggml_vec_mul_f32(ne0,
-                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
-                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
-                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+            ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
 #endif
                 // }
             // }
         }
     } else {
         // src1 is not contiguous
-        for (int ir = ith; ir < nr; ir += nth) {
-            // src0, src1 and dst are same shape => same indices
-            const int i3 = ir/(ne2*ne1);
-            const int i2 = (ir - i3*ne2*ne1)/ne1;
-            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
-
-            float * dst_ptr  = (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
-            float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
-            for (int i0 = 0; i0 < ne0; i0++) {
-                float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+
+            for (int64_t i0 = 0; i0 < ne00; i0++) {
+                float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
 
                 dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
             }