import math, os if __name__ == "__main__": os.environ["DEFAULT_FLOAT"] = "bfloat16" os.environ["OPTIM_DTYPE"] = "bfloat16" if "DEV" not in os.environ: os.environ["DEV"] = "NULL::gfx950" # CDNA os.environ["DEVICE_IN_FUNCTION_BUG"] = "1" os.environ["ALL2ALL"] = "1" os.environ["USE_ATOMICS"] = "1" if "HK_FLASH_ATTENTION" not in os.environ: os.environ["HK_FLASH_ATTENTION"] = "1" if "ASM_GEMM" not in os.environ: os.environ["ASM_GEMM"] = "1" from tinygrad import Tensor, nn, function, getenv, dtypes, TinyJit from tinygrad.helpers import Timing, colored, GlobalCounters, profile_marker, round_up from tinygrad.uop.ops import Ops, UOp from extra.models.llama import apply_rotary_emb, precompute_freqs_cis from extra.llama_kernels.rmsnorm import rmsnorm ASM_GEMM = getenv("ASM_GEMM", 0) FUSED_SILU_W13 = getenv("FUSED_SILU_W13", 0) SPLIT_W13 = getenv("SPLIT_W13", 0) MXFP4 = getenv("MXFP4", 0) def matmul(x:Tensor, w:Tensor, mxfp4:bool=bool(MXFP4), mxfp4_w:tuple[Tensor, Tensor, Tensor, Tensor]|None=None, x_prequant_mxfp4:tuple[Tensor|None, Tensor|None, Tensor|None, Tensor|None]|None=None) -> tuple[Tensor,...]: if mxfp4 or ASM_GEMM: from extra.gemm.cdna_asm_gemm import asm_gemm, can_use_asm_gemm if can_use_asm_gemm(x, w.T): return (asm_gemm(x, w.T, mxfp4=mxfp4, mxfp4_w=mxfp4_w, mxfp4_x=x_prequant_mxfp4),) return (x @ w.T,) def norm_quantize_matmul(x:Tensor, norm:Tensor, w:Tensor, eps:float, mxfp4_w=None): x_normed, rrms = rmsnorm(x, eps) out, *ret = matmul(x_normed * norm, w, mxfp4_w=mxfp4_w) return out, x_normed, rrms, ret def add_norm_quantize_matmul(x:Tensor, residual:Tensor, norm:Tensor, w:Tensor, eps:float, mxfp4_w=None): h = x + residual x_normed, rrms = rmsnorm(h, eps) out, *ret = matmul(x_normed * norm, w, mxfp4_w=mxfp4_w) return out, h, x_normed, rrms, ret def silu_w13_quantize_matmul(x_w13:Tensor, w2:Tensor, mxfp4_w=None): if FUSED_SILU_W13 and MXFP4: from extra.llama_kernels.swiglu import swiglu_mxfp4 x2, x2_mxfp4 = swiglu_mxfp4(x_w13) out, *ret = matmul(x2, w2, mxfp4_w=mxfp4_w, x_prequant_mxfp4=x2_mxfp4) return out, ret hidden = x_w13.shape[-1] // 2 x_w1, x_w3 = x_w13[..., :hidden], x_w13[..., hidden:] out, *ret = matmul(x_w1.silu() * x_w3, w2, mxfp4_w=mxfp4_w) return out, ret class FlatTransformer: def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_layers:int, norm_eps:float, vocab_size:int, n_kv_heads:int|None=None, rope_theta:int=10000, max_context:int=1024): self.vocab_size = vocab_size self.n_layers = n_layers self.n_heads = n_heads self.n_kv_heads = n_kv_heads if n_kv_heads is not None else n_heads # n_kv_heads != n_heads implies MQA [arxiv/2307.09288, A.2.1] self.head_dim = dim // n_heads self.n_rep = self.n_heads // self.n_kv_heads self.hidden_dim = hidden_dim scaled_std = 0.02 / math.sqrt(2 * n_layers) # Attention self.wqkv = self.lin_per_layer(dim, self.n_heads * self.head_dim + self.n_kv_heads * self.head_dim * 2) self.wo = self.lin_per_layer(self.n_heads * self.head_dim, dim, std=scaled_std) # FeedForward if SPLIT_W13: self.w1 = self.lin_per_layer(dim, hidden_dim) self.w3 = self.lin_per_layer(dim, hidden_dim) else: self.w13 = self.lin_per_layer(dim, hidden_dim * 2) self.w2 = self.lin_per_layer(hidden_dim, dim, std=scaled_std) self.norm_eps = norm_eps self.attention_norm = Tensor.ones(n_layers, dim).contiguous() self.ffn_norm = Tensor.ones(n_layers, dim).contiguous() # output self.norm = nn.RMSNorm(dim, norm_eps) self.tok_embeddings = nn.Embedding(vocab_size, dim) self.tok_embeddings.weight = Tensor.normal(vocab_size, dim, mean=0.0, std=0.02, dtype=dtypes.bfloat16) self.output = Tensor.normal(1, vocab_size, dim, mean=0.0, std=0.02, dtype=dtypes.bfloat16) self.freqs_cis = precompute_freqs_cis(dim // n_heads, max_context * 2, rope_theta).clone().is_param_(False) def lin_per_layer(self, in_features:int, out_features:int, std:float=0.02, w:Tensor|None=None): if w is None: if getenv("ZEROS"): w = Tensor.zeros(self.n_layers, out_features, in_features) else: w = Tensor.normal(self.n_layers, out_features, in_features, mean=0.0, std=std) return w.cast(dtypes.bfloat16) def attention(self, x:Tensor, freqs_cis:Tensor, *, attention_norm:Tensor, wqkv:Tensor, wo:Tensor, mxfp4_wqkv=None, mxfp4_wo=None): bsz, seqlen, _ = x.shape saves = [] xqkv, x_normed, rrms, s = norm_quantize_matmul(x, attention_norm, wqkv, self.norm_eps, mxfp4_w=mxfp4_wqkv) saves.extend([x_normed, rrms, *s, xqkv]) if getenv("HK_FLASH_ATTENTION"): from extra.thunder.amd.fa import flash_attention, fused_qkv_rope xq, xk, xv = fused_qkv_rope(xqkv, freqs_cis, self.n_heads, self.n_kv_heads, self.head_dim) attn, *save = flash_attention(xq, xk, xv, is_causal=True, write_flat=True) saves.extend(save) else: xqkv = xqkv.reshape(bsz, seqlen, self.n_kv_heads, self.n_rep + 2, self.head_dim) xq = xqkv[:, :, :, :self.n_rep].reshape(bsz, seqlen, self.n_heads, self.head_dim) xk = xqkv[:, :, :, self.n_rep].reshape(bsz, seqlen, self.n_kv_heads, self.head_dim) xv = xqkv[:, :, :, self.n_rep+1].reshape(bsz, seqlen, self.n_kv_heads, self.head_dim) xq, xk = apply_rotary_emb(xq, xk, freqs_cis) xq, xk, xv = xq.cast(dtypes.bfloat16), xk.cast(dtypes.bfloat16), xv.cast(dtypes.bfloat16) xq, xk, xv = xq.transpose(1, 2), xk.transpose(1, 2), xv.transpose(1, 2) attn = xq.scaled_dot_product_attention(xk, xv, is_causal=True, enable_gqa=True).transpose(1, 2) attn = attn.reshape(bsz, seqlen, -1) out, *s = matmul(attn, wo, mxfp4_w=mxfp4_wo) saves.extend([*s, out]) return out, saves def feed_forward(self, x:Tensor, residual:Tensor, **kwargs): saves = [] if SPLIT_W13: h = x + residual x_normed, rrms = rmsnorm(h, self.norm_eps) saves.extend([x_normed, rrms]) inp = x_normed * kwargs["ffn_norm"] x_w1, *s = matmul(inp, kwargs["w1"], mxfp4_w=kwargs.get("mxfp4_w1")) saves.extend([*s, x_w1]) x_w3, *s = matmul(inp, kwargs["w3"], mxfp4_w=kwargs.get("mxfp4_w3")) saves.extend([*s, x_w3]) out, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], mxfp4_w=kwargs.get("mxfp4_w2")) saves.extend([*s, out]) else: x_w13, h, x_normed, rrms, s = add_norm_quantize_matmul(x, residual, kwargs["ffn_norm"], kwargs["w13"], self.norm_eps, mxfp4_w=kwargs.get("mxfp4_w13")) saves.extend([x_normed, rrms, *s, x_w13]) out, s = silu_w13_quantize_matmul(x_w13, kwargs["w2"], mxfp4_w=kwargs.get("mxfp4_w2")) saves.extend([*s, out]) return out, h, saves @function(precompile=True, precompile_backward=True) def run_layer(self, x:Tensor, freqs_cis:Tensor, attn_kwargs:dict, ffn_kwargs:dict, save:bool=True): attn, attn_saves = self.attention(x, freqs_cis, **attn_kwargs) ffn, h, ffn_saves = self.feed_forward(x, attn, **ffn_kwargs) h = h + ffn if save: return (h, *attn_saves, *ffn_saves) else: return (h,) def shard(self, device:tuple[str, ...], mp:bool=False): from tinygrad.nn.state import get_parameters if not mp: for v in get_parameters(self): v.shard_(device, axis=None) else: # flat per-layer weights: axis 0 is n_layers, so shard axes are +1 vs per-layer Transformer self.wqkv.shard_(device, axis=1).realize() # (n_layers, out, dim) shard out self.wo.shard_(device, axis=2).realize() # (n_layers, dim, in) shard in if SPLIT_W13: self.w1.shard_(device, axis=1).realize() self.w3.shard_(device, axis=1).realize() else: self.w13.shard_(device, axis=1).realize() # (n_layers, hidden*2, dim) shard out self.w2.shard_(device, axis=2).realize() # (n_layers, dim, hidden) shard in self.attention_norm.shard_(device, axis=None).realize() self.ffn_norm.shard_(device, axis=None).realize() self.norm.weight.shard_(device, axis=None).realize() self.tok_embeddings.weight.shard_(device, axis=0).realize() self.output.shard_(device, axis=1).realize() self.freqs_cis.shard_(device, axis=None).realize() def create_mxfp4_weight_cache(self) -> dict[str, list[tuple[Tensor, Tensor, Tensor, Tensor]]]: assert MXFP4 from extra.llama_kernels.quantize_mxfp4 import quantize_mxfp4 names = ("wqkv", "wo", "w1", "w3", "w2") if SPLIT_W13 else ("wqkv", "wo", "w13", "w2") return {name:[quantize_mxfp4(w, shuffle_row=True, shuffle_col=True) for w in getattr(self, name)] for name in names} def update_mxfp4_weight_cache(self, cache:dict[str, list[tuple[Tensor, Tensor, Tensor, Tensor]]]) -> list[Tensor]: from extra.llama_kernels.quantize_mxfp4 import quantize_mxfp4 return [x for name, layers in cache.items() for weight, outputs in zip(getattr(self, name), layers) for x in quantize_mxfp4(weight, shuffle_row=True, shuffle_col=True, out=outputs)] def __call__(self, tokens:Tensor, save:bool=True, mxfp4_weights:dict[str, list[tuple[Tensor, Tensor, Tensor, Tensor]]]|None=None): h = self.tok_embeddings(tokens) freqs_cis = self.freqs_cis.cast(h.dtype) if not getenv("HK_FLASH_ATTENTION"): freqs_cis = freqs_cis[:, :tokens.shape[1], :, :, :] for i in range(self.n_layers): attn_kwargs = dict(attention_norm=self.attention_norm[i], wqkv=self.wqkv[i], wo=self.wo[i]) ffn_kwargs = dict(ffn_norm=self.ffn_norm[i], w2=self.w2[i]) if mxfp4_weights is not None: attn_kwargs.update(mxfp4_wqkv=mxfp4_weights["wqkv"][i], mxfp4_wo=mxfp4_weights["wo"][i]) ffn_kwargs.update(mxfp4_w2=mxfp4_weights["w2"][i]) if SPLIT_W13: ffn_kwargs.update(w1=self.w1[i], w3=self.w3[i]) if mxfp4_weights is not None: ffn_kwargs.update(mxfp4_w1=mxfp4_weights["w1"][i], mxfp4_w3=mxfp4_weights["w3"][i]) else: ffn_kwargs.update(w13=self.w13[i]) if mxfp4_weights is not None: ffn_kwargs.update(mxfp4_w13=mxfp4_weights["w13"][i]) h, *_ = self.run_layer(h, freqs_cis, attn_kwargs, ffn_kwargs, save=save) logits = matmul(self.norm(h), self.output[0], mxfp4=False)[0] return logits def _get_pads(uop:UOp) -> list[UOp]: if uop.op == Ops.ADD: return _get_pads(uop.src[0]) + _get_pads(uop.src[1]) return [uop] def apply_grad(grad_buf:Tensor, new_grad:UOp): pads = _get_pads(new_grad) if len(pads) <= 1: new_grad = new_grad.cast(grad_buf.dtype) grad_buf.uop = grad_buf.uop.after(grad_buf.uop.store(grad_buf.uop + new_grad)) return cur = grad_buf.uop for pad in sorted(pads, key=lambda p: p.marg[0][0] if p.op == Ops.PAD else 0, reverse=True): if pad.op == Ops.PAD: grad_shrink = tuple([(p[0], s+p[0]) for s,p in zip(pad.src[0].shape, pad.marg)]) buf_slice = cur.shrink(grad_shrink) cur = cur.after(buf_slice.store(buf_slice + pad.src[0].cast(cur.dtype))) else: cur = cur.after(cur.store(cur + pad.cast(cur.dtype))) grad_buf.uop = cur if __name__ == "__main__": config = {} BS = config["BS"] = getenv("BS", 16) SEQLEN = config["SEQLEN"] = getenv("SEQLEN", 8192) SMALL = config["SMALL"] = getenv("SMALL", 0) from examples.llama3 import MODEL_PARAMS model_params = MODEL_PARAMS[llama_size:=getenv("LLAMA3_SIZE", "8B")]["args"] # vocab_size from mixtral tokenizer if not SMALL: model_params |= {"vocab_size": 32000} real_vocab_size = model_params['vocab_size'] if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: model_params["n_layers"] = llama_layers # pad vocab if (MP := getenv("MP", 1)) > 1: model_params["vocab_size"] = round_up(model_params["vocab_size"], 256 * MP) vocab_mask:Tensor = Tensor.arange(model_params["vocab_size"]).reshape(1, 1, -1) >= real_vocab_size model = FlatTransformer(**model_params, max_context=SEQLEN) state = nn.state.get_state_dict(model) print("tensor count:", len(state)) # shard the model from tinygrad import Device is_dp = (DP := getenv("DP", 1)) > 1 is_mp = (MP := getenv("MP", 1)) > 1 is_sharding = is_dp or is_mp device_count = max(DP, MP) device = tuple(f"{Device.DEFAULT}:{i}" for i in range(device_count)) model.shard(device, is_mp) if is_dp: vocab_mask.shard_(device, axis=None).realize() if is_mp: vocab_mask.shard_(device, axis=2).realize() # preallocate all the grad buffers and zero them out grads = {x:x.zeros_like().contiguous() for x in state.values() if x.is_param} # print model size sz = 0 for k,v in state.items(): print(f"{colored(k, 'green' if v in grads else 'white'):30s} {str(v.shape):30s} {str(v.dtype):20s} {v.device} {v.nbytes()/1e9:.2f} GB") sz += v.nbytes() print(f"total sz: {sz/1e9:.2f} GB") with Timing("fake data: "): tokens = Tensor.randint(BS, SEQLEN+1, low=0, high=real_vocab_size, dtype=dtypes.int) with Timing("realize weights/grads/data: "): Tensor.realize(*state.values(), *grads.values(), tokens) print("mem per device: " + ', '.join(f"{dev}: {mem/1e9:.2f} GB" for dev, mem in sorted(GlobalCounters.mem_used_per_device.items()))) if DP > 1: tokens = tokens.shard(tuple(f"{Device.DEFAULT}:{i}" for i in range(DP)), axis=0) if MP > 1: tokens = tokens.shard(tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))) @TinyJit def fwd_bwd(tokens:Tensor): with Timing("python forward: "): logits = model(tokens[:, :-1], save=llama_size=="8B") loss = vocab_mask.where(-1e9, logits).sparse_categorical_crossentropy(tokens[:, 1:]) with Timing("python backward: "): for t,g in zip(grads, loss.gradient(*grads)): apply_grad(grads[t], g.uop) with Timing("run fwd_bwd: "): loss.realize(*grads.values()) @TinyJit def optim_step(): for g in grads.values(): g.assign(g.zeros_like()) Tensor.realize(*grads.values()) for i in range(6): GlobalCounters.reset() profile_marker(f"step {i}") with Timing(colored(f"*** step {i}: ", "red")): fwd_bwd(tokens) optim_step() print("mem per device: " + ', '.join(f"{dev}: {mem/1e9:.2f} GB" for dev, mem in sorted(GlobalCounters.mem_used_per_device.items())))