ggml-backend update: buffer types, backend registry, graph compare, tests #620

slaren · 2023-11-23T21:46:27Z

Buffer types

Buffers are no longer tied to a backend instance
A buffer may be used in multiple backends. In systems with unified memory or integrated GPUs, this can allow fallback to CPU without copies
Backends may add different types of buffers, even for different backends. For example, ggml-cuda adds a host pinned buffer that can be used with the CPU backend for faster transfer of inputs and outputs between system and device memory.

Backend registry

Allows enumerating and initializing the available backends.

// enumerate backends
for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
    printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
    ggml_backend_buffer_t buf = ggml_backend_buft_alloc_buffer(ggml_backend_reg_get_default_buffer_type(i), size);
    ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
    // ...
}

// initialize a backend by name
ggml_backend_t backend = ggml_backend_reg_init_backend_from_str("CUDA0"); // cuda device 0

Graph copy & compare between backends

Copy a graph to a different backend and evaluate it on both one op at a time. A callback can be used to compare the results of each operation.

// graph is allocated on the CUDA backend: copy and compare with CPU backend
auto eval_callback = [](int index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data) {
    if (t1->type == GGML_TYPE_F32) {
        std::vector<float> v1(GGML_PAD(ggml_nbytes(t1), sizeof(float)) / sizeof(float));
        std::vector<float> v2(GGML_PAD(ggml_nbytes(t2), sizeof(float)) / sizeof(float));

        ggml_backend_tensor_get(t1, v1.data(), 0, ggml_nbytes(t1));
        ggml_backend_tensor_get(t2, v2.data(), 0, ggml_nbytes(t2));

        for (size_t i = 0; i < v1.size(); ++i) {
            if (fabsf(v1[i] - v2[i]) > 1e0) {
                printf("[%d] %s [%s]: mismatch at index %zu: %f != %f\n", index, t1->name, ggml_op_desc(t1), i, v1[i], v2[i]);
                return false; // stop graph eval on first error
            }
        }
    }
    return true;
};

ggml_backend_compare_graph_backend(model.backend, backend_cpu, gf, eval_callback, nullptr);

`ggml_backend_alloc_ctx_tensors`

Allocate all the tensors in a context and a backend buffer in one step. Ref: #578

model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

model.wte     = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
model.wpe     = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
// etc

// allocate the model tensors in a backend buffer
model.buffer_w = ggml_backend_alloc_ctx_tensors(ctx, model.backend);

`ggml_unary_op_name` and `ggml_op_desc`

ggml_op_desc can be used as a replacement of ggml_op_name that also returns the name of the unary op when the op is GGML_OP_UNARY.

CUDA multiple device support

ggml_backend_cuda_init takes a int device parameter that specifies the CUDA device to use. To use the default device, pass 0. Each device is registered as a different backend in the backend registry, with names CUDA0 for device 0, CUDA1 for device 1 and so on.

Backend op tests

Each op implemented in the backends is tested against the CPU backend. Tensors are initialized with random data, and the result of the op is compared using a normalized MSE. New ops implemented in the backends should add a test in test-backend-ops.cpp. Only F16/F32 tests for now, quantized types are not yet supported in the test.

FSSRepo · 2023-11-23T23:17:59Z

Nice upate, Normally, one has to create the graph at every moment before computation; it cannot be reused, and simply assigning IDs to input tensors wouldn't suffice.

         ggml_allocr_reset(compute_alloc);

        struct ggml_cgraph * gf = build_graph(z, decode_graph);
        ggml_allocr_alloc_graph(compute_alloc, gf);

        if (ggml_backend_is_cpu(backend)) {
            ggml_backend_cpu_set_n_threads(backend, n_threads);
        }

        ggml_backend_graph_compute(backend, gf);

        ggml_backend_tensor_get(gf->nodes[gf->n_nodes - 1], work_result->data, 0, ggml_nbytes(work_result));

ggml_allocr_reset(compute_alloc);
ggml_set_input(gf, compute_alloc, Z_INPUT, z);

ggml_allocr_alloc_graph(compute_alloc, gf);

 if (ggml_backend_is_cpu(backend)) {
          ggml_backend_cpu_set_n_threads(backend, n_threads);
}

 // gf is created once, it can be the graph used for measure buffer size
ggml_backend_graph_compute(backend, gf);

ggml_backend_tensor_get(gf->nodes[gf->n_nodes - 1], work_result->data, 0, ggml_nbytes(work_result));

ggml_backend_alloc_ctx_tensors(ctx, model.backend); Skip already allocated tensors, for example when assigning a constant tensor such as the attention query scale.

// constant tensor alloc it once time when initialize the params of model
attn_scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
        ggml_allocr_alloc(alloc, attn_scale);
        float scale = 1.0f / sqrt((float) d_head);
        ggml_backend_tensor_set(attn_scale, &scale, 0, sizeof(scale));

// to avoid assert(data == NULL)
// alloc all tensors linked to this context
        for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
           if(t->data == NULL) {
             ggml_allocr_alloc(alloc, t);
           }
        }

ggml_backend_compare_graph_backend wonderful function, thank you, It will be very useful for debugging dequantization kernel issues since, for some reason, in stable diffusion, quantizations work fine for me in the CPU backend but not in CUDA. At some point, I start to get NaN, and I have evidence that it used to work before, but with some change in the CUDA backend, it stopped working.

If possible, it could be beneficial for parameter tensors to be dynamically passed in a backend (dynamic offloading) as needed during computation.

slaren · 2023-11-24T14:02:38Z

Nice upate, Normally, one has to create the graph at every moment before computation; it cannot be reused, and simply assigning IDs to input tensors wouldn't suffice.

I think that what you mean here is that graphs used for measure cannot be re-used for computation, which is not very good for graphs that only need to be evaluated once. I think we could solve this by assigning offsets rather than absolute addresses to tensors (ie. in the data member), then it would be possible to replace the buffer used to allocate the graph without having to update every tensor. However, this would break compatibility with the CPU backend, and it would require significant changes to ggml.c, which is something that we have tried to avoid for now. Maybe in the future we can re-evaluate this, but it is not going to happen soon.

ggml_backend_alloc_ctx_tensors(ctx, model.backend); Skip already allocated tensors, for example when assigning a constant tensor such as the attention query scale.

I will do this.

ggml_backend_compare_graph_backend wonderful function, thank you, It will be very useful for debugging dequantization kernel issues since, for some reason, in stable diffusion, quantizations work fine for me in the CPU backend but not in CUDA.

The goal is indeed to help debugging issues with the backends. I already found some differences in the GELU op between the CPU and CUDA backends while testing this with the gpt-2 example, and I am sure that there are more cases. I will try to add automated tests for all the ops.

If possible, it could be beneficial for parameter tensors to be dynamically passed in a backend (dynamic offloading) as needed during computation.

I don't really understand what you mean by this. If you prefer to use Spanish, you can send me a message to the email address in my github profile.

FSSRepo · 2023-11-24T14:45:22Z

some differences in the GELU op

comment #define GGML_GELU_FP16 in ggml.c and try again

slaren · 2023-11-28T16:13:39Z

@ggerganov is the github CI capable of using the Metal backend? I suspect that it is failing somewhere during ggml_metal_init.

19: Test command: /Users/runner/work/ggml/ggml/build/bin/test-backend-buffer
19: Working Directory: /Users/runner/work/ggml/ggml/build/tests
19: Environment variables: 
19:  LLVM_PROFILE_FILE=test-backend-buffer.profraw
19: Test timeout computed to be: 900
19: ggml_backend_register: registered backend CPU
19: ggml_backend_register: registered backend Metal
19: ggml_metal_init: allocating
19: ggml_metal_init: found device: Apple Paravirtual device
19: ggml_metal_init: picking default device: Apple Paravirtual device
19: ggml_metal_init: default.metallib not found, loading from source
19: ggml_metal_init: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd
19: ggml_metal_init: loading 'ggml-metal.metal'
19/19 Test #19: test-backend-buffer ..............***Exception: SegFault  0.04 sec
Errors while running CTest

ggerganov · 2023-11-28T17:14:56Z

Hm, seems it does not work, but it does not print any meaningful error.
Probably we should just disable it for now and use ggml-ci for test-backend-buffer?

slaren · 2023-11-28T17:21:37Z

I am not sure what is happening either, it seems that the log is missing the output from stdout too. Let's disable it for now then, thanks for looking into it.

slaren · 2023-11-29T16:06:15Z

@FSSRepo can you help me define a few test cases for im2col? I need different values of parameters to test, currently it is tested with these (the default values in the constructor):

// GGML_OP_IM2COL
struct test_im2col : public test_case {
    const ggml_type type_a;
    const ggml_type type_b;
    const std::array<int64_t, 4> ne_a;
    const std::array<int64_t, 4> ne_b;
    const int s0;
    const int s1;
    const int p0;
    const int p1;
    const int d0;
    const int d1;
    const bool is_2D;

    std::string vars() override {
        return VARS_TO_STR11(type_a, type_b, ne_a, ne_b, s0, s1, p0, p1, d0, d1, is_2D);
    }

    test_im2col(ggml_type type_a = GGML_TYPE_F16, ggml_type type_b = GGML_TYPE_F32,
            std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
            std::array<int64_t, 4> ne_b = {10, 10, 10, 10},
            int s0 = 1, int s1 = 1,
            int p0 = 0, int p1 = 0,
            int d0 = 1, int d1 = 1,
            bool is_2D = false)
        : type_a(type_a), type_b(type_b), ne_a(ne_a), ne_b(ne_b), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}

    ggml_tensor * build_graph(ggml_context * ctx) override {
        ggml_tensor * a = ggml_new_tensor(ctx, type_a, 4, ne_a.data());
        ggml_tensor * b = ggml_new_tensor(ctx, type_b, 4, ne_b.data());
        ggml_tensor * out = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, is_2D);
        return out;
    }
};

FSSRepo · 2023-11-29T17:24:40Z

My mistake, I gave you the wrong order of the tensors. Wait a little bit.

FSSRepo · 2023-11-29T17:27:18Z

Fixed:

// GGML_OP_IM2COL
struct test_im2col : public test_case {
    const ggml_type type_input;
    const ggml_type type_kernel;
    const std::array<int64_t, 4> ne_input;
    const std::array<int64_t, 4> ne_kernel;
    // stride
    const int s0;
    const int s1;
    // padding
    const int p0;
    const int p1;
    // dilatation
    const int d0;
    const int d1;
    // mode
    const bool is_2D;

    std::string vars() override {
        return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D);
    }

    test_im2col(ggml_type type_input = GGML_TYPE_F16, ggml_type type_kernel = GGML_TYPE_F32,
            std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
            std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
            int s0 = 1, int s1 = 1,
            int p0 = 1, int p1 = 1,
            int d0 = 1, int d1 = 1,
            bool is_2D = true)
        : type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}

    ggml_tensor * build_graph(ggml_context * ctx) override {
        ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
        ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
        ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D);
        return out;
    }
};

…ilures

slaren · 2023-11-29T17:32:31Z

I had to swap the kernel and input types, but otherwise it works.

ggml-ci

slaren · 2023-11-29T19:00:05Z

The ggml-ci is also failing with Metal because it cannot find the ggml-metal.metal, what would be the best way to fix that?

It is still going to fail after that because some ops are broken in Metal when the number of columns is not a multiple of 4 and there aren't checks for that, and additionally both CUDA and Metal have broken support for broadcasting with add and mul, but I think we should keep these tests enabled to remind us to fix it.

ggerganov · 2023-11-29T20:41:20Z

Will take a detailed look tomorrow. Great work on the tests - I was just thinking about something like this. Much needed.

ggml-ci

ggerganov · 2023-11-30T12:12:41Z

Regarding multiple CUDA devices: is it fully functional at this point? Is there going to be a conflict around the shared memory pool if it is used by more than one device or thanks to the lock everything should be good?

With this infrastructure, if let's say I wanted to implement an alternative backend for an existing GPU (for example, ggml-cuda-2.cu or ggml-metal-2.m), I can just register it as a new backend and both implementations should be usable together in a single binary, correct?

slaren · 2023-11-30T12:21:39Z

I wouldn't say that it is fully functional already because I haven't run enough tests to be confident that everything works correctly, but it should work. The memory pool is already per-device, so that shouldn't cause issues. There are still most definitely synchronization issues in the CUDA backend, so it is not possible to use two devices simultaneously (eg. in different threads). It should work for splitting a model to multiple devices with ggml_backend_sched, but I have not tested it, and there are still some optimizations that could be done (mainly, copies between devices will have happen as device->cpu->device in ggml-backend, rather than just doing a cudaMemcpy).

With this infrastructure, if let's say I wanted to implement an alternative backend for an existing GPU (for example, ggml-cuda-2.cu or ggml-metal-2.m), I can just register it as a new backend and both implementations should be usable together in a single binary, correct?

Yes, I don't see why that wouldn't work, except maybe for the few hooks that some backends (cuda/opencl) have in ggml.c. Ultimately the goal is to be able to use a single binary for all the backends, and detect the availability at runtime.

ggerganov · 2023-11-30T12:42:35Z

How come none of the MUL_MAT tests pass for the Metal backend:

Error: MUL_MAT: NMSE = 5.200507
  MUL_MAT(type_a=f32,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[1,1]): FAIL
Error: MUL_MAT: NMSE = 5.312463
  MUL_MAT(type_a=f32,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[2,1]): FAIL
Error: MUL_MAT: NMSE = 7.459601
  MUL_MAT(type_a=f32,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[1,2]): FAIL
Error: MUL_MAT: NMSE = 7.485240
  MUL_MAT(type_a=f32,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[2,2]): FAIL
Error: MUL_MAT: NMSE = 5.304665
  MUL_MAT(type_a=f16,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[1,1]): FAIL
Error: MUL_MAT: NMSE = 5.417071
  MUL_MAT(type_a=f16,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[2,1]): FAIL
Error: MUL_MAT: NMSE = 7.481416
  MUL_MAT(type_a=f16,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[1,2]): FAIL
Error: MUL_MAT: NMSE = 7.437984
  MUL_MAT(type_a=f16,type_b=f32,m=32,n=32,k=32,bs=[10,10],nr=[2,2]): FAIL

Edit: got it - dim[3] is not supported

ggerganov

Great stuff! Merge at will

…zing tensors without ggml-alloc

ggml-ci

ggml-backend update

cba20ed

slaren added 6 commits November 25, 2023 20:57

update metal backend

0627bc6

show metal logs with ggml-backend

56b748b

move buffer types to functions

e347921

cuda: add per-device backends

6412161

cuda: add host buffer type

6badc40

fix metal build

22ab6a4

slaren force-pushed the backend-v-next branch from 5443094 to 22ab6a4 Compare November 28, 2023 00:07

slaren added 2 commits November 28, 2023 15:51

ggml_backend_alloc_ctx_tensors : ignore allocated tensors

9d35334

ggml_backend_compare_graph_backend fixes

3ab1c91

ggerganov added 2 commits November 28, 2023 18:56

ci : try to fix metal build

3b8c69e

metal : first print device info, then build kernels

f7febe3

ci : disable GGML_METAL on Github Actions

c9e244b

ggerganov force-pushed the backend-v-next branch from 13fdba2 to c9e244b Compare November 28, 2023 17:15

slaren added 3 commits November 29, 2023 00:33

test-backend-ops initial impl (unary and get_rows)

a599a4a

more op tests

f18867e

cleanup

d3db8e3

slaren force-pushed the backend-v-next branch from ad7a50b to d3db8e3 Compare November 29, 2023 01:32

slaren added 2 commits November 29, 2023 16:53

print test params, add more tests cases for add and mul

59b7768

add tests for im2col

f56a458

slaren added 2 commits November 29, 2023 17:27

better f16 init

42124ab

metal : add basic impl of supports_op

aa00adf

slaren marked this pull request as ready for review November 29, 2023 16:47

update im2col test params, show callstack with GGML_ASSERT on CUDA fa…

66ae6d6

…ilures

slaren added 3 commits November 29, 2023 19:26

add more rope tests

ac2eb84

add more rope and mul_mat test cases

96c2aaa

add more get_rows test cases

5b855c0

ggml-ci

slaren and others added 2 commits November 30, 2023 10:10

add more norm and rms_norm test cases with different eps

a101aa1

ci : fix metal resource path

1999164

ggml-ci

ggerganov force-pushed the backend-v-next branch from 19ca35e to 1999164 Compare November 30, 2023 11:35

tests : silence warning

e3069f6

ggerganov approved these changes Nov 30, 2023

View reviewed changes

slaren added 8 commits November 30, 2023 15:33

add ggml_backend_tensor_alloc and ggml_backend_view_init for initiali…

8c5ef10

…zing tensors without ggml-alloc

add mul_mat test cases without dims 3 and 4

23d7d74

ggml-ci

check for nans and infs

f48a1c5

ggml-ci

add diag_mask_inf test cases without dims 3 and 4

ea0323b

ggml-ci

fix cuda leak while backend reg

8d381d6

fix msvc issues

c55a13e

remove backend_sched debug causes by default

424cdfa

gpt-2 : increase graph size

19cc60f

ggml-ci

slaren merged commit 38f46af into master Nov 30, 2023
7 of 9 checks passed

slaren deleted the backend-v-next branch November 30, 2023 18:03

This was referenced Dec 1, 2023

Fix crash when ggml_backend_metal_init() fails on old Apple device. ggerganov/whisper.cpp#1582

Closed

ggml-cuda : fix usage without CUDA devices #627

Merged

CCLDArjun pushed a commit to CCLDArjun/ggml that referenced this pull request Dec 18, 2023

ggml : fix NEON signs (close ggerganov#620, ggerganov#622)

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ggml-backend update: buffer types, backend registry, graph compare, tests #620

ggml-backend update: buffer types, backend registry, graph compare, tests #620

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ggml-backend update: buffer types, backend registry, graph compare, tests #620

ggml-backend update: buffer types, backend registry, graph compare, tests #620

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slaren commented Nov 23, 2023 • edited Loading

Buffer types

Backend registry

Graph copy & compare between backends

ggml_backend_alloc_ctx_tensors

ggml_unary_op_name and ggml_op_desc

CUDA multiple device support

Backend op tests

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`ggml_backend_alloc_ctx_tensors`

`ggml_unary_op_name` and `ggml_op_desc`

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