DarthAffe/stable-diffusion.cpp
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photomaker working properly now after merging and adapting to GGMLBlock API

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bssrdf committed 2024-02-26 13:32:19 -05:00
1 parent 4ac3df145e
commit 77b6a42bc3
4 files changed
+64 -70
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clip.hpp
+9 -30
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@@ -548,16 +548,7 @@ public:
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
// struct ggml_tensor* r = x;
struct ggml_tensor* h = layer_norm1->forward(ctx, x);
h = self_attn->forward(ctx, h, mask);
// x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, h);
x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
}
@@ -567,16 +558,10 @@ public:
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
// struct ggml_tensor* r = x;
struct ggml_tensor* h = layer_norm1->forward(ctx, x);
h = self_attn->forward(ctx, h, mask);
// x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, h);
// struct ggml_tensor* h = layer_norm1->forward(ctx, x);
// h = self_attn->forward(ctx, h, mask);
x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
// x = ggml_add(ctx, x, h);
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
}
@@ -614,10 +599,8 @@ public:
break;
}
std::string name = "layers." + std::to_string(i);
// printf(" about to do %s\n", name.c_str());
auto layer = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
x = layer->forward(ctx, x, mask, atten1); // [N, n_token, d_model]
// print_ggml_tensor(x, true, ("layer "+std::to_string(i)).c_str());
// LOG_DEBUG("layer %d", i);
}
return x;
@@ -924,21 +907,17 @@ public:
auto post_layernorm = std::dynamic_pointer_cast<LayerNorm>(blocks["post_layernorm"]);
auto x = embeddings->forward(ctx, pixel_values); // [N, num_positions, embed_dim]
print_ggml_tensor(x, true, "embedding");
x = pre_layernorm->forward(ctx, x);
print_ggml_tensor(x, true, "pre_layernorm");
x = encoder->forward(ctx, x, -1, false, false);
print_ggml_tensor(x, true, "encoder");
x = post_layernorm->forward(ctx, x); // [N, n_token, hidden_size]
print_ggml_tensor(x, true, "post_layernorm");
GGML_ASSERT(x->ne[3] == 1);
// int64_t max_token_idx = 0;
// ggml_tensor* pooled = ggml_view_1d(ctx, x, x->ne[0], x->nb[1] * max_token_idx); // assert N == 1
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));
// ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], 0);
ggml_tensor* pooled = ggml_view_2d(ctx, x, x->ne[0], x->ne[1], x->nb[1], 0);
print_ggml_tensor(pooled, true, "pooled");
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));
ggml_tensor* pooled = ggml_view_2d(ctx, x, x->ne[0], x->ne[1], x->nb[0], 0);
pooled = ggml_scale(ctx, ggml_cont(ctx, pooled), 1.f);
// print_ggml_tensor(pooled, true, "pooled");
return pooled; // [N, projection_dim]
}