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1 files changed, 167 insertions, 0 deletions

diff --git a/llama.cpp/common/debug.cpp b/llama.cpp/common/debug.cpp
new file mode 100644
index 0000000..0df409a
--- /dev/null
+++ b/llama.cpp/common/debug.cpp
@@ -0,0 +1,167 @@
+#include "debug.h"
+
+#include "log.h"
+
+#include <cmath>
+#include <string>
+
+static std::string common_ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
+        }
+    }
+    return str;
+}
+
+static float common_ggml_get_float_value(const uint8_t * data,
+                           ggml_type       type,
+                           const size_t *  nb,
+                           size_t          i0,
+                           size_t          i1,
+                           size_t          i2,
+                           size_t          i3) {
+    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
+    float  v;
+    if (type == GGML_TYPE_F16) {
+        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
+    } else if (type == GGML_TYPE_F32) {
+        v = *(const float *) &data[i];
+    } else if (type == GGML_TYPE_I64) {
+        v = (float) *(const int64_t *) &data[i];
+    } else if (type == GGML_TYPE_I32) {
+        v = (float) *(const int32_t *) &data[i];
+    } else if (type == GGML_TYPE_I16) {
+        v = (float) *(const int16_t *) &data[i];
+    } else if (type == GGML_TYPE_I8) {
+        v = (float) *(const int8_t *) &data[i];
+    } else if (type == GGML_TYPE_BF16) {
+        v = ggml_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
+    } else {
+        GGML_ABORT("fatal error");
+    }
+    return v;
+}
+
+#define INDENT "    "
+
+template <bool abort>
+void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+    GGML_ASSERT(n > 0);
+    float sum = 0;
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    sum += v;
+                }
+            }
+        }
+    }
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        LOG(INDENT "[\n");
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2 * n) {
+                LOG(INDENT INDENT "..., \n");
+                i2 = ne[2] - n;
+            }
+            LOG(INDENT INDENT "[\n");
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2 * n) {
+                    LOG(INDENT INDENT INDENT "..., \n");
+                    i1 = ne[1] - n;
+                }
+                LOG(INDENT INDENT INDENT "[");
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2 * n) {
+                        LOG("   ..., ");
+                        i0 = ne[0] - n;
+                    }
+                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    LOG("%12.4f", v);
+                    if (i0 < ne[0] - 1) {
+                        LOG(", ");
+                    }
+                }
+                LOG("  ],\n");
+            }
+            LOG(INDENT INDENT "],\n");
+        }
+        LOG(INDENT "]\n");
+        LOG(INDENT "sum = %f\n", sum);
+    }
+
+    if constexpr (abort) {
+        if (std::isnan(sum)) {
+            LOG("encountered NaN - aborting\n");
+            exit(0);
+        }
+    }
+}
+
+/**
+ * GGML operations callback during the graph execution.
+ *
+ * @param t current tensor
+ * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
+ *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
+ *            see ggml_backend_sched_eval_callback
+ * @param user_data user data to pass at each call back
+ * @return true to receive data or continue the graph, false otherwise
+ */
+template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (base_callback_data *) user_data;
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+
+    if (ask) {
+        return true;  // Always retrieve data
+    }
+
+    bool matches_filter = cb_data->tensor_filters.empty();
+
+    if (!matches_filter) {
+        for (const auto & filter : cb_data->tensor_filters) {
+            if (std::regex_search(t->name, filter)) {
+                matches_filter = true;
+                break;
+            }
+        }
+    }
+
+    char src1_str[128] = { 0 };
+    if (src1) {
+        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, common_ggml_ne_string(src1).c_str());
+    }
+
+    if (matches_filter) {
+        LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
+            ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
+            common_ggml_ne_string(t).c_str());
+    }
+
+    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
+
+    if (!is_host) {
+        auto n_bytes = ggml_nbytes(t);
+        cb_data->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+    }
+
+    if (!ggml_is_quantized(t->type) && matches_filter) {
+        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
+        common_debug_print_tensor<abort_on_nan>(data, t->type, t->ne, t->nb, 3);
+    }
+
+    return true;
+}
+
+// Explicit template instantiations
+template bool common_debug_cb_eval<false>(ggml_tensor *, bool, void *);
+template bool common_debug_cb_eval<true>(ggml_tensor *, bool, void *);
+template void common_debug_print_tensor<false>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
+template void common_debug_print_tensor<true>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);