llama: match flat_llama with model_train (#16269)

examples/mlperf/models/flat_llama.py

@@ -12,7 +12,7 @@ if __name__ == "__main__":
     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
+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
@@ -290,20 +290,36 @@ 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"]
-  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: model_params['n_layers'] = llama_layers
+  # 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
-  if (DP := getenv("DP", 1)) > 1:
-    model.shard(tuple(f"{Device.DEFAULT}:{i}" for i in range(DP)))
-  if (MP := getenv("MP", 1)) > 1:
-    model.shard(tuple(f"{Device.DEFAULT}:{i}" for i in range(MP)), mp=True)
+  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
   grad_dtype = lambda x: dtypes.bfloat16 if x.dtype in dtypes.fp8s else x.dtype
@@ -320,7 +336,7 @@ if __name__ == "__main__":
     sz += v.nbytes()
   print(f"total sz: {sz/1e9:.2f} GB")
 
-  with Timing("fake data: "): tokens = Tensor.randint(BS, SEQLEN+1, low=0, high=model.vocab_size, dtype=dtypes.int)
+  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)
@@ -328,7 +344,9 @@ if __name__ == "__main__":
 
   @TinyJit
   def fwd_bwd(tokens:Tensor):
-    with Timing("python forward: "): loss = model(tokens[:, :-1], save=llama_size=="8B").sparse_categorical_crossentropy(tokens[:, 1:])
+    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)

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_run_mp.sh

@@ -4,7 +4,6 @@ export PYTHONPATH="."
 export PATH="/opt/rocm-7.1.1/bin:$PATH"
 export ROCM_PATH="/opt/rocm-7.1.1"
 export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
 export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
 export DEVICE_IN_FUNCTION_BUG=1