metal : bug with ggml_cont

[ggerganov]

This is supposed to work, but it breaks the results with Metal enabled:

diff --git a/llama.cpp b/llama.cpp
index c97c146..097de72 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2418,11 +2418,11 @@ static struct ggml_cgraph * llm_build_llama(
 
             // split cached V into n_head heads
             struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
+                ggml_cont(ctx0, ggml_view_3d(ctx0, kv_self.v,
                         n_past + N, n_embd_head, n_head_kv,
                         ggml_element_size(kv_self.v)*n_ctx,
                         ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il));
             offload_func_v(V);
             ggml_set_name(V, "V");

Need to understand why and fix it

[ggerganov]

@lshzh-ww

Disabling the concurrency optimization makes it work, but I can't find what is the problem.
Any ideas?

I suspect it might be related to the recent change for tensor views, but not sure: 06abf8e
cc @slaren for insights

[ggerganov]

This fixes it:

--- a/llama.cpp
+++ b/llama.cpp
@@ -2423,6 +2423,7 @@ static struct ggml_cgraph * llm_build_llama(
                         ggml_element_size(kv_self.v)*n_ctx,
                         ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
                         ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il));
+            V->src[1] = KQ_soft_max;
             offload_func_v(V);
             ggml_set_name(V, "V");

It seems the concurrency detection does not take into account the indirect dependency of V on v.
It thinks that kv_self.v is a leaf and basically puts all GGML_OP_CONT at the start of the graph execution.

What would be a good solution for this problem?
Should we finally obsolete ggml_build_forward() and add ggml_depend()?

[ggerganov]

Need confirmation if this is correct.

Btw, I see that check_mem is always set to false.
Are there cases where we would still need it?
If it's not needed, should we remove it?

[slaren]

Makes sense, I had a similar problem when implementing parallel execution with CUDA, and at the time my solution was this:
https://github.com/ggerganov/llama.cpp/blob/d273bfd2c98148fab309f978c44ec2e8c24d1c4d/llama.cpp#L1294-L1305
And then using k and v instead of kv_self.

I don't really like the solution of ggml_depend, I think that eventually we should find a way to do this automatically, but for now it should do.

[ggerganov]

I added your proposal since it better expresses the KV cache dependency, until we figure out a better solution.

For this to work though, we need to set the ggml_view_tensor op to GGML_OP_VIEW as you have recently noted. I guess this change is fine

[slaren]

In this case, we should also set src[0] to src in ggml_view_tensor, and add the additional dependency as src[1] instead in llm_build_graph. Otherwise, it would break old code that assumes that the source is in src[0], such as this in ggml-cuda:
https://github.com/ggerganov/llama.cpp/blob/bd33e5ab92e7f214205792fc1cd9ca28e810f897/ggml-cuda.cu#L6539-L6544
ggml_graph_import may also be an issue, and I am not sure that it will import the second dependency in src[1] correctly, since it just creates a new tensor calling ggml_view_4d. Newer code should use view_src instead.

[ggerganov]

Ah good points. I guess I'll come back to this later and make a better solution.
Made an issue so we don't forget: ggml-org/ggml#502

[YixinSong-e]

Makes sense, I had a similar problem when implementing parallel execution with CUDA, and at the time my solution was this:

https://github.com/ggerganov/llama.cpp/blob/d273bfd2c98148fab309f978c44ec2e8c24d1c4d/llama.cpp#L1294-L1305

And then using k and v instead of kv_self.
I don't really like the solution of ggml_depend, I think that eventually we should find a way to do this automatically, but for now it should do.

Hello ! Could you share your code for implementing parallel execution with CUDA?

[cebtenzzre]

Hello ! Could you share your code for implementing parallel execution with CUDA?

See PR #2239, which the linked code snippet is from.