[JAX] Add collective GEMM without compute/communication overlap #1675
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| # Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
| # | ||
| # See LICENSE for license information. | ||
| import pytest | ||
| from functools import partial | ||
| from collections.abc import Iterable | ||
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| import numpy as np | ||
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| import jax | ||
| import jax.numpy as jnp | ||
| from jax.sharding import Mesh, NamedSharding, PartitionSpec | ||
| from jax.experimental import mesh_utils | ||
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| import transformer_engine.jax as te | ||
| from transformer_engine.jax.gemm import gemm | ||
| from transformer_engine.jax.quantize import helper | ||
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| from utils import assert_allclose | ||
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| jax.config.update("jax_enable_compilation_cache", False) | ||
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| # AG+GEMM: (4, 32/P, 128) ----(AG)----> (4, 32, 128) x (128, 256/P) ----------> (4, 32, 256/P) | ||
| # - DGRAD: (4, 32, 256/P) x (128, 256/P)^T --(AR)--> (4, 32, 128) | ||
| # - WGRAD: (4, 32/P, 128)^T --(AG)--> (4, 32, 128)^T x (4, 32, 256/P) --------> (128, 256/P) | ||
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| # GEMM+AR: (4, 32, 256/P) x (256/P, 128) --(AR)--> (4, 32, 128) | ||
| # - DGRAD: (4, 32, 128) x (256/P, 128)^T ------> (4, 32, 256/P) | ||
| # - WGRAD: (4, 32, 256/P)^T --(AG)--> (4, 32, 256)^T x (4, 32, 128) --------> (256, 128) | ||
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| BATCH = 4 | ||
| BASE_SIZE = 16 | ||
| SEQ_LEN = BASE_SIZE * 8 | ||
| HIDDEN_SIZE = BASE_SIZE * 6 | ||
| FFN_HIDDEN_SIZE = BASE_SIZE * 16 | ||
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| COMM_TYPES = ["ALL_GATHER", "ALL_REDUCE"] | ||
| MESH_TYPES = ["FSDP_TP", "DP_TP", "TP"] | ||
| NUM_DEVICES = 4 | ||
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| is_fp8_supported, no_fp8_reason = helper.is_fp8_available() | ||
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| def _get_mesh(parallel_dist): | ||
| jax.clear_caches() | ||
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| batched = False | ||
| fsdp = False | ||
| mesh_shape = dict(tp=NUM_DEVICES) | ||
| resources = dict(cp_resource="tp", tp_resource="tp") | ||
| if parallel_dist in ["DP_TP", "FSDP_TP"]: | ||
| batched = True | ||
| tp = NUM_DEVICES // 2 | ||
| dp = NUM_DEVICES // tp | ||
| mesh_shape.update(dict(tp=tp, dp=dp)) | ||
| resources.update(dict(dp_resource="dp")) | ||
| if parallel_dist == "FSDP_TP": | ||
| fsdp = True | ||
| dp = 1 | ||
| zp = NUM_DEVICES // tp | ||
| mesh_shape.update(dict(tp=tp, dp=1, zp=zp)) | ||
| resources.update(dict(fsdp_resource="zp")) | ||
| mesh_resource = te.MeshResource(**resources) | ||
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| devices = mesh_utils.create_device_mesh((NUM_DEVICES,), devices=jax.devices()[:NUM_DEVICES]) | ||
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| mesh = Mesh(np.array(devices).reshape(tuple(mesh_shape.values())), tuple(mesh_shape.keys())) | ||
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| return mesh, mesh_resource, batched, fsdp | ||
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| def _get_inputs(mesh, mesh_resource, dtype, fwd_comm_type, batched, fsdp, fwd_bwd=False): | ||
| fp8_gemm = dtype in [jnp.float8_e4m3fn, jnp.float8_e5m2] | ||
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| # Operand and output shapes | ||
| lhs_shape = ( | ||
| [SEQ_LEN, HIDDEN_SIZE] if fwd_comm_type == "ALL_GATHER" else [SEQ_LEN, FFN_HIDDEN_SIZE] | ||
| ) | ||
| rhs_shape = ( | ||
| [HIDDEN_SIZE, FFN_HIDDEN_SIZE] | ||
| if fwd_comm_type == "ALL_GATHER" | ||
| else [FFN_HIDDEN_SIZE, HIDDEN_SIZE] | ||
| ) | ||
| out_shape = [lhs_shape[0], rhs_shape[1]] | ||
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| if batched: | ||
| lhs_shape = [BATCH] + lhs_shape | ||
| out_shape = [BATCH] + out_shape | ||
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| # Operand and output partition specs | ||
| lhs_spec = ( | ||
| [mesh_resource.tp_resource, None] | ||
| if fwd_comm_type == "ALL_GATHER" | ||
| else [None, mesh_resource.tp_resource] | ||
| ) | ||
| rhs_spec = ( | ||
| [None, mesh_resource.tp_resource] | ||
| if fwd_comm_type == "ALL_GATHER" | ||
| else [mesh_resource.tp_resource, None] | ||
| ) | ||
| out_spec = [None, rhs_spec[-1]] | ||
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| # Modify RHS operand for FP8 | ||
| fsdp_gathered_rhs_spec = rhs_spec.copy() | ||
| if fp8_gemm: | ||
| rhs_shape = list(reversed(rhs_shape)) | ||
| rhs_spec = list(reversed(rhs_spec)) | ||
| fsdp_gathered_rhs_spec = list(reversed(fsdp_gathered_rhs_spec)) | ||
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| # Add batch dimensions and specs | ||
| if batched: | ||
| if fsdp: | ||
| lhs_spec = [(mesh_resource.dp_resource, mesh_resource.fsdp_resource)] + lhs_spec | ||
| rhs_spec = [mesh_resource.fsdp_resource if spec is None else spec for spec in rhs_spec] | ||
| out_spec = [(mesh_resource.dp_resource, mesh_resource.fsdp_resource)] + out_spec | ||
| else: | ||
| lhs_spec = [mesh_resource.dp_resource] + lhs_spec | ||
| out_spec = [mesh_resource.dp_resource] + out_spec | ||
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| # Allocate global operands on device | ||
| key = jax.random.PRNGKey(42) | ||
| split_keys = jax.random.split(key, 3 if fwd_bwd else 2) | ||
| mu = 0.0 | ||
| sigma = 0.023 | ||
| shapes = (lhs_shape, rhs_shape) | ||
| if fwd_bwd: | ||
| shapes += (out_shape,) | ||
| global_operands = list( | ||
| map( | ||
| lambda key, shape: jax.device_put( | ||
| mu + (sigma * jax.random.normal(key, shape, dtype=dtype)), | ||
| NamedSharding(mesh, PartitionSpec(None)), | ||
| ), | ||
| split_keys, | ||
| shapes, | ||
| ) | ||
| ) | ||
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| # Allocate sharded operands on device | ||
| partition_axes = (lhs_spec, rhs_spec) | ||
| if fwd_bwd: | ||
| partition_axes += (out_spec,) | ||
| local_operands = list( | ||
| map( | ||
| lambda x, spec: jax.device_put(x, NamedSharding(mesh, PartitionSpec(*spec))), | ||
| global_operands, | ||
| partition_axes, | ||
| ) | ||
| ) | ||
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| # Tranpose global RHS back to non-transpoosed orientation if it was originally allocated | ||
| # for FP8 GEMM | ||
| if fp8_gemm: | ||
| rhs_global = jnp.matrix_transpose(global_operands[1]) | ||
| global_operands = (global_operands[0], rhs_global, *global_operands[2:]) | ||
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| return ( | ||
| local_operands, | ||
| global_operands, | ||
| (out_shape, out_spec), | ||
| fsdp_gathered_rhs_spec, | ||
| ) | ||
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| def _check_output(mesh, expected_out_shape, expected_out_specs, *tensors, fwd_bwd=False): | ||
| num_operands = 3 if fwd_bwd else 2 | ||
| ref_operands = tensors[:num_operands] | ||
| test_outputs = tensors[num_operands:] | ||
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| # Check number of dimensions | ||
| assert test_outputs[0].ndim == len(expected_out_shape), ( | ||
| f"Output has different number of dimensions ({test_outputs[0].ndim}) than expected " | ||
| + f"({len(expected_out_shape)})" | ||
| ) | ||
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| # Pad test output spec for unsharded dimensions | ||
| test_spec = te.sharding.get_padded_spec(test_outputs[0].sharding.spec, test_outputs[0].ndim) | ||
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| for i in range(test_outputs[0].ndim): | ||
| # Check shape | ||
| assert test_outputs[0].shape[i] == expected_out_shape[i], ( | ||
| f"Output with shape {test_outputs[0].shape} does not match expected shape " | ||
| + f"{expected_out_shape} in dimension index {i}." | ||
| ) | ||
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| # Check shardings (with padded output spec) | ||
| spec_mismatch = False | ||
| if isinstance(expected_out_specs[i], str): | ||
| if test_spec[i] != expected_out_specs[i]: | ||
| spec_mismatch = True | ||
| elif isinstance(expected_out_specs[i], Iterable): | ||
| if not isinstance(test_spec[i], type(expected_out_specs[i])): | ||
| if test_spec[i] not in expected_out_specs[i]: | ||
| spec_mismatch = True | ||
| elif len(test_spec[i]) != len(expected_out_specs[i]): | ||
| spec_mismatch = True | ||
| else: | ||
| for j in range(len(expected_out_specs[i])): | ||
| if test_spec[i][j] != expected_out_specs[i][j]: | ||
| spec_mismatch = True | ||
| break | ||
| elif expected_out_specs[i] == None: | ||
| if test_spec[i] != None: | ||
| spec_mismatch = True | ||
| else: | ||
| raise RuntimeError("Internal TE error: Unrecognized reference partition spec type.") | ||
| if spec_mismatch: | ||
| raise AssertionError( | ||
| f"Output sharding {test_spec} does not match expected sharding " | ||
| + f"{expected_out_specs} in dimension index {i}." | ||
| ) | ||
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| def _native_gemm_fwd_bwd(lhs, rhs, grad): | ||
| fwd_out, vjp_fn = jax.vjp(jnp.dot, lhs, rhs) | ||
| lhs_grad, rhs_grad = vjp_fn(grad) | ||
| return fwd_out, lhs_grad, rhs_grad | ||
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| ref_fn = jax.jit(_native_gemm_fwd_bwd if fwd_bwd else jnp.dot) | ||
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| out_names = ["output"] | ||
| ref_outputs = ref_fn(*ref_operands) | ||
| if not fwd_bwd: | ||
| ref_outputs = [ref_outputs] | ||
| else: | ||
| out_names += ["dgrad", "wgrad"] | ||
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| for i, (test_out, ref_out) in enumerate(zip(test_outputs, ref_outputs)): | ||
| test_out_global = jax.lax.with_sharding_constraint( | ||
| test_out, NamedSharding(mesh, PartitionSpec(None)) | ||
| ) | ||
| try: | ||
| assert_allclose(ref_out, test_out_global) | ||
| except AssertionError as err: | ||
| raise AssertionError(f"Numerical mismatch in {out_names[i]}:\n" + str(err)) | ||
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| @pytest.mark.parametrize("comm_type", COMM_TYPES) | ||
| @pytest.mark.parametrize("mesh_type", MESH_TYPES) | ||
| def test_gemm_impl(comm_type, mesh_type): | ||
| mesh, mesh_resource, batched, fsdp = _get_mesh(mesh_type) | ||
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| ( | ||
| local_operands, | ||
| global_operands, | ||
| output_info, | ||
| fsdp_gathered_rhs_spec, | ||
| ) = _get_inputs(mesh, mesh_resource, jnp.bfloat16, comm_type, batched, fsdp) | ||
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| @jax.jit | ||
| def _test_fn(lhs, rhs): | ||
| rhs_no_fsdp = jax.lax.with_sharding_constraint( | ||
| rhs, NamedSharding(mesh, PartitionSpec(*fsdp_gathered_rhs_spec)) | ||
| ) | ||
| return te.cpp_extensions.collective_gemm_impl(lhs, rhs_no_fsdp, batched_output=batched) | ||
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| with te.sharding.global_shard_guard(mesh_resource): | ||
| output, *_ = _test_fn(*local_operands) | ||
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| _check_output(mesh, *output_info, *global_operands, output) | ||
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| @pytest.mark.parametrize("comm_type", COMM_TYPES) | ||
| @pytest.mark.parametrize("mesh_type", MESH_TYPES) | ||
| def test_gemm_fwd_bwd(comm_type, mesh_type): | ||
| mesh, mesh_resource, batched, fsdp = _get_mesh(mesh_type) | ||
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| ( | ||
| local_operands, | ||
| global_operands, | ||
| output_info, | ||
| fsdp_gathered_rhs_spec, | ||
| ) = _get_inputs(mesh, mesh_resource, jnp.bfloat16, comm_type, batched, fsdp, fwd_bwd=True) | ||
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| @jax.jit | ||
| def _test_fn(lhs, rhs, grad): | ||
| # Gather weights in FSDP axis | ||
| rhs_no_fsdp = jax.lax.with_sharding_constraint( | ||
| rhs, NamedSharding(mesh, PartitionSpec(*fsdp_gathered_rhs_spec)) | ||
| ) | ||
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| # FWD pass | ||
| fwd_out, vjp_fn = jax.vjp(gemm, lhs, rhs_no_fsdp) | ||
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| # BWD pass | ||
| lhs_grad, rhs_grad = vjp_fn(grad) | ||
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| return fwd_out, lhs_grad, rhs_grad | ||
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| print( | ||
| f"INPUTS: {local_operands[0].shape} x {local_operands[1].shape}\n" | ||
| + f" LHS sharding: {local_operands[0].sharding.spec}\n" | ||
| + f" RHS sharding: {local_operands[1].sharding.spec}\n" | ||
| ) | ||
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| with te.sharding.global_shard_guard(mesh_resource): | ||
| output, dgrad, wgrad = _test_fn(*local_operands) | ||
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| print( | ||
| f"{'AG + GEMM' if comm_type == 'AG' else 'GEMM + AR'} output: " | ||
| + f"{output.shape} | {output.sharding.spec}\n" | ||
| + f"DGRAD: {dgrad.shape} | {dgrad.sharding.spec}\n" | ||
| + f"WGRAD: {wgrad.shape} | {wgrad.sharding.spec}\n" | ||
| ) | ||
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| _check_output(mesh, *output_info, *global_operands, output, dgrad, wgrad, fwd_bwd=True) |
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Hi,
Any reasons for these changes?